From dcfedc8dc57fb8e5e6b1cd65b456f01589a8b2ae Mon Sep 17 00:00:00 2001 From: xxl <505279206@qq.com> Date: Wed, 12 Mar 2025 16:58:03 +0800 Subject: [PATCH] first commit --- LICENSE | 316 + NOTICES | 209 + README.md | 546 +- config.json | 31 + generation_config.json | 7 + model-00001-of-00002.safetensors | 3 + model-00002-of-00002.safetensors | 3 + model.safetensors.index.json | 406 + modeling_instella.py | 1251 + scaling_perf_instruct.png | 3 + special_tokens_map.json | 24 + tokenizer.json | 250613 ++++++++++++++++++++++++++++ tokenizer_config.json | 248 + 13 files changed, 253658 insertions(+), 2 deletions(-) create mode 100644 LICENSE create mode 100644 NOTICES create mode 100644 config.json create mode 100644 generation_config.json create mode 100644 model-00001-of-00002.safetensors create mode 100644 model-00002-of-00002.safetensors create mode 100644 model.safetensors.index.json create mode 100644 modeling_instella.py create mode 100644 scaling_perf_instruct.png create mode 100644 special_tokens_map.json create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..e74770c --- /dev/null +++ b/LICENSE @@ -0,0 +1,316 @@ +Instella-3B [RESEARCH-ONLY RAIL-MS] + +Licensed Artifact(s): + +- Model + +- Source Code + +Section I: PREAMBLE + +BY ACCESSING, DOWNLOADING, INSTALLING, OR USING THE ARTIFACT, YOU AGREE +TO BE BOUND BY THIS LICENSE. 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The Artifact +is subject to additional terms and conditions as described in Section III +below. + +2. Grant of Copyright License. Conditioned upon compliance with Section +III below and subject to the terms and conditions of this License, each +Contributor hereby grants to You, only in connection with the Permitted +Purpose, a worldwide, non-exclusive, royalty-free copyright license to +reproduce, use, publicly display, publicly perform, sublicense, and +distribute the Artifact and Derivatives thereof. + +3. Grant of Patent License. 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If any provision of this License is held to be invalid, illegal or +unenforceable, the remaining provisions shall be unaffected thereby and +remain valid as if such provision had not been set forth herein. + +12. Term and Termination. The term of this License will commence upon +the earlier of Your (a) acceptance of this License or (b) accessing the +Artifact; and will continue in full force and effect until terminated in +accordance with the terms and conditions herein. Licensor may terminate +this License if You are in breach of any term or condition of this +License. Upon termination of this License, all licenses granted to You +will terminate and You must promptly delete and cease use of the +Artifact. Sections 1, 7, 8, 9, 10, 11, and 12 survive termination of +this License. + +END OF TERMS AND CONDITIONS + +Attachment A + +AMD Responsible AI Use Policy + +AMD is committed to the responsible use of its Artificial Intelligence +(AI) products and technologies (“AMD AI”). AMD AI may include +artificial intelligence or machine learning technologies that use +algorithms to analyze data and generate output using predictions based +on patterns in data. This policy explains the uses that AMD +specifically prohibits. + +If you use any AMD AI, you are agreeing to use the AMD AI in compliance +with applicable laws and not for any of the following prohibited uses. + +Prohibited Uses: + +1) No Illegal Acts. Do not use AMD AI in violation of any applicable +national, state, local, or other jurisdictional law, rule, regulation, +or sanction. + +2) No Explicit Content. Do not use AMD AI to submit (as input), +generate, or disseminate content depicting violent or sexually explicit +content or to create sexual chatbots. + +3) No Harm. Do not use AMD AI for any potentially harmful uses, + including fraud, deception, discrimination, abuse, or harassment, + including the following: + + a) Harm or abuse of a minor, including grooming and child sexual + exploitation. + + b) Impersonation of human beings for purposes of deception. + + c) Generation or dissemination of information you know to be false + for the purpose of harming others. + + d) Intentionally defame, disparage, or otherwise harass others. + + e) Intentionally attempting to materially distort the behavior of a + person in a manner that causes or is likely to cause that person + or another person physical or psychological harm. + + f) Providing medical advice or interpretation of medical results that + is intended to be a substitute for professional medical advice, + diagnosis, or treatment. + + g) Engaging in the unlawful or unauthorized practice of any + profession, including financial, legal, medical, health, or + related professional practices. + + h) Judgment of, discrimination against, or harm to individuals or + groups based on legally protected characteristics or categories, + online or offline social behavior, or known or predicted personal + or personality characteristics, including any of the foregoing + uses in social credit systems. + +4) No High-Risk Activity. Do not use AMD AI in any high-risk activities + or applications that create a risk of personal injury, death, or +severe property or environmental damage, including in weapons or +military applications. + +5) No Personal Information. Do not use AMD AI to collect, process, or +disclose personal data, including heath or sensitive personal +information, without the necessary rights or consents. + +6) No Infringement. Do not use AMD AI to generate or disseminate any +information that infringes upon or misappropriates the intellectual +property rights of others, including copyright, trademark, patent, and +trade secret rights, rights to privacy, and publicity rights. + +7) No Malware. Do not use AMD AI to generate or disseminate malware or +any other content to be used for the purpose of facilitating unpermitted +access to, or use of, computer systems or data. + +8) No Obfuscation. 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Instella models outperform existing fully open models of similar sizes and achieve competitive performance compared to state-of-the-art open-weight models such as Llama-3.2-3B, Gemma-2-2B, and Qwen-2.5-3B, including their instruction-tuned counterparts. + +
+ +Figure 1: Pareto frontier of pre-training tokens vs average performance for pre-trained and instruction-tuned models. +
+ +By training Instella from scratch on Instinct MI300X GPUs, we highlight our hardware’s capability and scalability in handling demanding large-scale AI training workloads, offering a viable alternative in the AI hardware landscape. In line with the AMD commitment to open source, we are releasing all artifacts related to Instella models [here](#additional-resources), including the model weights, detailed training configurations, datasets, and code, enabling the AI community to collaborate, replicate, and innovate, thereby accelerating progress. + +## Takeaways + +- **Announcing Instella**, a series of 3 billion parameter language models developed by AMD, trained from scratch on 128 Instinct MI300X GPUs. +- **Instella models significantly outperform existing fully open LMs** (Figure 1) of comparable size, as well as bridge the gap between fully open and open weight models by achieving competitive performance compared state-of-the-art open weight models and their instruction-tuned counterparts. +- Fully open and accessible: **Fully open-source release of model weights, training hyperparameters, datasets, and code**, fostering innovation and collaboration within the AI community. +- Supported by the AMD ROCm software stack, Instella employs efficient training techniques such as **FlashAttention-2, Torch Compile, and Fully Sharded Data Parallelism (FSDP)** with hybrid sharding to **scale model training over a large cluster.** + +## Instella Models + +In this release, we introduce the following Instella models: +
+ +| Model | Stage | Training Data (Tokens) | Description | +| :----: | :----: | :----: | :---- | +| [Instella-3B-Stage1](https://huggingface.co/amd/Instella-3B-Stage1) | Pre-training (Stage 1) | 4.065 Trillion | First stage pre-training to develop proficiency in natural language. | +| [Instella-3B](https://huggingface.co/amd/Instella-3B) | Pre-training (Stage 2) | 57.575 Billion | Second stage pre-training to further enhance problem solving capabilities. | +| [Instella-3B-SFT](https://huggingface.co/amd/Instella-3B-SFT) | SFT | 8.902 Billion (x3 epochs) | Supervised Fine-tuning (SFT) to enable instruction-following capabilities. | +| [Instella-3B-Instruct](https://huggingface.co/amd/Instella-3B-instruct) | DPO | 760 Million | Alignment to human preferences and strengthen chat capabilities with direct preference optimization (DPO). | +| | **Total:** | **4.15 Trillion** | | + +Table 1: Instella models and training stages. +
+ +The Instella models are text-only, autoregressive transformer-based LMs having 3 billion parameters. Architecture-wise, Instella is packed with 36 decoder layers, each having 32 attention heads. These models support a sequence length of up to 4,096 tokens and have a vocabulary size of ~50,000 tokens using the OLMo tokenizer. During both pre-training and fine-tuning, we utilized FlashAttention-2, Torch Compile, and bfloat16 mixed-precision training to reduce memory usage, leading to computational speedups and optimal resource utilization. To balance inter-node memory efficiency and intra-node communication overhead within our cluster, we employed fully sharded data parallelism (FSDP) with hybrid sharding, with model parameters, gradients, and optimizer states sharded within a node and replicated across the nodes. + +Our training pipeline is based on the open-sourced OLMo codebase, adapted, and optimized for our hardware and model architecture. For pre-training we used a total of 128 Instinct MI300X GPUs distributed across 16 nodes with each node having 8x Instinct MI300X GPUs. We evaluated our models and baselines using standard tasks from [OLMES](https://github.com/allenai/olmes/tree/main), [FastChat MT-Bench](https://github.com/lm-sys/FastChat/blob/main/fastchat/llm_judge/README.md), and [Alpaca](https://github.com/tatsu-lab/alpaca_eval/tree/main). For more details about the architecture, training pipeline/hyperparameters and evaluation results, please refer to our [Blog](https://rocm.blogs.amd.com/artificial-intelligence/introducing-instella-3B/README.html), [Hugging Face model card](https://huggingface.co/amd/Instella-3B) and [Github repository](https://github.com/AMD-AIG-AIMA/Instella). + +## Training Pipeline + +The training of the Instella models comprised of four stages, where each stage incrementally enhanced the model’s capabilities from fundamental natural language understanding to instruction following and alignment towards human preferences. + +### Model Summary + +| Stage | Model | Training Tokens | Layers | Attention Heads | Model Hidden Size | MLP Hidden Size | Context Length | RoPE Theta | +| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | +| Pre-training | Instella-3B-stage1 | 4.065T | 36 | 32 | 2560 | 13824 | 4096 | 10,000 | +| Pre-training | Instella-3B | 57.575B | 36 | 32 | 2560 | 13824 | 4096 | 10,000 | +| SFT | Instella-3B-SFT | 8.902B (x3) | 36 | 32 | 2560 | 13824 | 4096 | 10,000 | +| SFT+DPO | Instella-3B-instruct | 760M | 36 | 32 | 2560 | 13824 | 4096 | 10,000 | + +### Hyparparameter + +|Stage | Optimizer | Peak LR | LR Scheduler | Alpha F | Warmup (steps) | Weight Decay | Decay Norm & Bias | Decay Embedding | Batch Size (Tokens) | Epochs | +|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:|-----:| +| Pretraining Stage 1 | AdamW(0.9,0.95) | 4.0e-4 | cosine_with_warmup | 0.1 | 2000 | 0.1 | True | True | 4M | 1 | +| Pretraining Stage 2 | AdamW(0.9,0.95) | 4.0e-5 | cosine_with_warmup | 0.0 | 0 | 0.1 | True | True | 4M | 1 | +| SFT | AdamW(0.9,0.95) | 1.0e-5 | linear_with_warmup | 0.001 | 500 | 0.1 | True | True | 0.5M | 3 | +| DPO | AdamW(0.9,0.95) | 5.0e-7 | linear | -- | 10% | 0.1 | -- | -- | 0.25M | 1 | + +## Getting Started + +### Installation + +First, install [PyTorch](https://pytorch.org) according to the instructions specific to your operating system. For AMD GPUs, you can also start with a [rocm/pytorch](https://hub.docker.com/r/rocm/pytorch/tags?name=pytorch) docker. + +To install from source (recommended for training/fine-tuning) run: + +```bash +git clone https://github.com/AMD-AIG-AIMA/Instella.git +cd Instella +# install Flash-Attention on MI300X +GPU_ARCH=gfx942 MAX_JOBS=$(nproc) pip install git+https://github.com/Dao-AILab/flash-attention.git -v +# install other dependencies +pip install -e .[all] +``` + +### Example Usage + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer +checkpoint = "amd/Instella-3B-Instruct" + +tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True) +model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", trust_remote_code=True) + +prompt = [{"role": "user", "content": "What are the benefits of open-source AI research?"}] +inputs = tokenizer.apply_chat_template( + prompt, + add_generation_prompt=True, + return_tensors='pt' +) + +tokens = model.generate( + inputs.to(model.device), + max_new_tokens=1024, + temperature=0.8, + do_sample=True +) + +print(tokenizer.decode(tokens[0], skip_special_tokens=False)) +``` + +### Chat in TRL +You can also use the TRL CLI to chat with the model from the terminal: +```bash +pip install trl +trl chat --model_name_or_path amd/Instella-3B-Instruct --trust_remote_code --max_new_tokens 1024 + +# : +# which is bigger 9.8 or 9.11? + +# : +# 9.8 is bigger than 9.11. The difference between the two numbers is 0.69 (9.8 - 9.11 = 0.69), which indicates that 9.8 is 0.69 units larger than 9.11. +``` + +## Results + +### Pre-training + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelsSizeTraining TokensAvgARC ChallengeARC EasyBoolQHellaswagPiQASciQWinnogradeOpenBookQAMMLUBBH (3-shot)GSM8k (8-shot)
Open Weight Models
Gemma-2-2B2.61B~2T59.3439.4659.3074.5070.5076.4096.6069.8044.8053.2840.7527.37
Llama-3.2-3B3.21B~9T62.5147.1664.9174.8073.1075.9095.3070.3051.2057.8147.0030.10
Qwen2.5-3B3.09B~18T68.3051.5167.1979.1072.1077.4095.5069.3051.4067.2256.6963.84
Fully Open Models
Pythia-2.8b2.91B300B49.8340.4760.7064.8060.1072.5089.7060.8042.6026.0927.692.73
GPTNeo-2.7B2.72B~420B47.9638.4654.5662.7055.2070.8088.0058.3040.8027.8327.253.71
OpenELM-3B3.04B~1.5T52.2837.4658.4268.6071.7075.6092.5065.4046.4026.6929.402.96
StableLM-3B-4E1T2.8B~4T58.5144.8267.0275.4074.2078.4093.4068.4048.6045.1937.3310.84
Instella-3B-Stage13.11B~4T61.3353.8573.1678.7074.2077.5094.9071.2051.4054.6934.3010.77
Instella-3B3.11B~4T+60B66.5952.8470.5376.5075.0077.8096.4073.1052.4058.3139.7459.82
+ Table 2: Pre-trained model performance on standard benchmarks. Here Bold represents the best performance, and Underscore represents the second best performance. +
+ +- Both Instella-3B-Stage1 & Instella-3B models outperform all the other fully open models over all the benchmarks individually (except PIQA). **Our final pre-trained checkpoint Instella-3B outperforms the existing top performant fully open pre-trained models by a lead of ⬆️8.08% on average**, with significant improvements in `ARC Challenge [+8.02%], ARC Easy [+3.51%], Winnograde [+4.7%], OpenBookQA [+3.88%], MMLU [+13.12%] and ️GSM8K [+48.98%]`. +- **Second stage pre-training elevated the overall average performance relative to stage-1 by ⬆️5.26%**, substantially narrowing the performance gap between Instella-3B model vs the closed-source models, and **outperforming Llama-3.2-3B by ⬆️4.08% on average** (`+5.69% [ARC Challenge], +5.61% [ARC Easy], and +29.72% [GSM8k]`), **Gemma-2-2B by ⬆️7.25% on average** (`+13.38% [ARC Challenge], +11.23% [ARC Easy], +4.5% [Hellaswag], +7.6% [OpenBookQA], +5.03% [MMLU], and +32.45% [GSM8k]`), and is **competitive with Qwen-2.5-3B** on the majority of the benchmarks. +- The multi-stage pre-training with diverse and high-quality data mix significantly enhanced Instella-3B’s capabilities, establishing it as a competitive and open alternative in the landscape of comparable size language models. + +### Instruction-tuning Results + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelsSizeTraining TokensAvgMMLUTruthfulQABBHGPQAGSM8KMinerva MATHIFEvalAlpacaEval 2MT-Bench
Open Weight Models
Gemma-2-2B-Instruct2.61B~2T39.0458.3555.7642.9625.2253.4522.4855.6429.418.07
Llama-3.2-3B-Instruct3.21B~9T47.5361.5050.2361.5029.6977.0346.0075.4219.317.13
Qwen2.5-3B-Instruct3.09B~18T48.7266.9057.1657.2928.1375.9760.4262.4822.128.00
Fully Open Models
StableLM-zephyr-3B2.8B4T30.5045.1047.9039.3225.6758.3810.3834.207.516.04
OpenELM-3B-Instruct3.04B~1.5T14.1127.3638.0824.2418.081.590.3816.080.211.00
Instella-3B-SFT3.11B~4T42.0558.7652.4946.0028.1371.7240.5066.177.587.07
Instella-3B-Instruct3.11B~4T44.8758.9055.4746.7530.1373.9242.4671.3517.597.23
+ Table 2: Instruct model performance on standard benchmarks. Here Bold represents the best performance, and Underscore represents the second best performance. +
+ +- **Instella-3B-Instruct model consistently outperforms other fully open models across all evaluated benchmarks with a significant average score lead of ⬆️ 14.37%** w.r.t the next top performing fully open instruction-tuned models. With substantial margins across all the chat benchmarks (`+13% [MMLU], 7.57% [TruthfulQA], 7.43% [BBH], +4.46% [GPQA], +37.15 [IFEval], 10.08% [Alpaca 2], and 1.2% [MT-Bench]`). +- **Instella-3B-Instruct narrows the performance gap with leading open-weight models.** Instella-3B-Instruct performs **on par with or slightly surpasses existing state-of-the-art open weight instruction-tuned models** such as Llama-3.2-3B-Instruct (`+5.24% [TruthfulQA], 0.45% [GPQA], and +0.1% [MT-Bench]`), and Qwen2.5-3B-Instruct (`+2.01% [GPQA] and +8.87% [IFEval]`), while significantly outperforming Gemma-2-2B-Instruct with an average score lead of ⬆️5.83% (`+0.55% [MMLU], +3.79 [BBH], +4.91 [GPQA], +20.47 [GSM8k], +19.98 [Minerva MATH], and +15.17% [IFEval]`). +- **Overall, Instella-3B-Instruct excels in instruction following tasks and multi-turn QA tasks like TruthfulQA, GPQA, IFEval and MT-Bench**, while being highly competitive compared to existing state-of-the-art open weight models on other knowledge recall and math benchmarks, while being trained on significantly fewer training tokens. + +## Training Data + +| Stage | Model | Dataset | License | +| :---- | :---- | :---- | :---- | +| Pre-training Stage 1 | Instella-3B-stage1 | [https://huggingface.co/datasets/allenai/OLMoE-mix-0924](https://huggingface.co/datasets/allenai/OLMoE-mix-0924) | ODC-BY-1.0 | +| Pre-training Stage 2 | Instella-3B | [https://huggingface.co/datasets/allenai/tulu-3-sft-mixture](https://huggingface.co/datasets/allenai/tulu-3-sft-mixture) | ODC-BY-1.0 | +| Pre-training Stage 2 | Instella-3B | [https://huggingface.co/datasets/allenai/dolmino-mix-1124](https://huggingface.co/datasets/allenai/dolmino-mix-1124) | ODC-BY-1.0 | +| Pre-training Stage 2 | Instella-3B | [https://huggingface.co/datasets/teknium/OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) | Refer source materials | +| Pre-training Stage 2 | Instella-3B | [https://huggingface.co/datasets/TIGER-Lab/WebinstructSub](https://huggingface.co/datasets/TIGER-Lab/WebinstructSub) | Apache-2.0 | +| Pre-training Stage 2 | Instella-3B | [https://huggingface.co/datasets/m-a-p/Code-Feedback](https://huggingface.co/datasets/m-a-p/Code-Feedback) | Apache-2.0 | +| Pre-training Stage 2 | Instella-3B | [https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) | MIT | +| Pre-training Stage 2 | Instella-3B | [https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus/viewer/python-edu](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus/viewer/python-edu) | ODC-BY-1.0 | +| Pre-training Stage 2 | Instella-3B | [https://github.com/google-deepmind/mathematics_dataset](https://github.com/google-deepmind/mathematics_dataset) | Apache-2.0 | +| Pre-training Stage 2 | Instella-3B | [https://huggingface.co/datasets/amd/Instella-GSM8K-synthetic](https://huggingface.co/datasets/amd/Instella-GSM8K-synthetic) | [LICENSE](https://huggingface.co/datasets/amd/Instella-GSM8K-synthetic/blob/main/LICENSE) | +| SFT | Instella-3B-SFT | [https://huggingface.co/datasets/nvidia/OpenMathinstruct-2](https://huggingface.co/datasets/nvidia/OpenMathinstruct-2) | CC-BY-4.0 | +| SFT | Instella-3B-SFT | [https://huggingface.co/datasets/cais/mmlu](https://huggingface.co/datasets/cais/mmlu) | MIT | +| SFT | Instella-3B-SFT | [https://huggingface.co/datasets/HuggingFaceTB/smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) | Apache-2.0 | +| SFT | Instella-3B-SFT | [https://huggingface.co/datasets/GAIR/o1-journey](https://huggingface.co/datasets/GAIR/o1-journey) | Refer source materials | +| SFT | Instella-3B-SFT | [https://huggingface.co/datasets/allenai/tulu-3-sft-personas-instruction-following (subset of Tulu3)](https://huggingface.co/datasets/allenai/tulu-3-sft-personas-instruction-following) | ODC-BY-1.0 | +| DPO | Instella-3B-instruct | [https://huggingface.co/datasets/allenai/olmo-2-1124-7b-preference-mix](https://huggingface.co/datasets/allenai/olmo-2-1124-7b-preference-mix) | ODC-BY-1.0 | + +> [!NOTE] +> Further information concerning the training datasets, including applicable licensing terms and use restrictions, may be located at the linked source location. + +## Conclusion + +The release of the Instella family of models represents a significant stride in advancing open-source AI and demonstrating the capabilities of AMD hardware in large-scale language model training. The 3 billion parameter models from Instella family significantly outperform present fully open comparable size models in key benchmarks while also being competitive to comparable open-weight models, which we attribute to the high-quality data-mix selection, multi-stage training pipeline, and the use of high-performance Instinct MI300X GPUs for large scale training. + +By fully open sourcing the Instella models, including weights, training configurations, datasets, and code, we aim to foster innovation and collaboration within the AI community. We believe that transparency, reproducibility and accessibility are key drivers of progress in AI research and development. We invite developers, researchers, and AI enthusiasts to explore Instella, contribute to its ongoing improvement, and join us in pushing the boundaries of what is possible with language models. + +We will continue enhancing the models across multiple dimensions, including context length, reasoning ability, and multimodal capabilities. Additionally, we will scale up both the model and dataset while exploring diverse architectural approaches. Keep your eyes peeled for more exciting blogs on the Instella LMs family, its features and capabilities! + +## Additional Resources + +### Hugging Face Model Cards + +- Pre-trained models: + - Instella-3B-Stage1: [amd/Instella-3B-Stage1](https://huggingface.co/amd/Instella-3B-Stage1), First stage pre-training checkpoint. + - Instella-3B: [amd/Instella-3B](https://huggingface.co/amd/Instella-3B), Final pre-training checkpoint. +- Instruction-tuned models: + - Instella-3B-SFT: [amd/Instella-3B-SFT](https://huggingface.co/amd/Instella-3B-SFT), Supervised fine-tuned checkpoint. + - Instella-3B-Instruct: [amd/Instella-3B-Instruct](https://huggingface.co/amd/Instella-3B-Instruct), Final Instruction-tuned checkpoint. + +### Datasets + +Second stage pre-training GSM8k synthetic dataset: [amd/Instella-GSM8K-synthetic](https://huggingface.co/datasets/amd/Instella-GSM8K-synthetic) + +- The dataset consists of two splits: `train` and `train_119K`. +- For Instella-3B model second stage pre-training we used the `train_119K` split, which is a subset of the larger `train` split. + +### Code + +- Github: [https://github.com/AMD-AIG-AIMA/Instella](https://github.com/AMD-AIG-AIMA/Instella) + +Please refer to the following blogs to get started with using these techniques on AMD GPUs: + +- [PyTorch Fully Sharded Data Parallel (FSDP) on AMD GPUs with ROCm™](https://rocm.blogs.amd.com/artificial-intelligence/fsdp-training-pytorch/README.html) +- [Accelerating Large Language Models with Flash Attention on AMD GPUs](https://rocm.blogs.amd.com/artificial-intelligence/flash-attention/README.html) +- [Accelerate PyTorch Models using torch.compile on AMD GPUs with ROCm™](https://rocm.blogs.amd.com/artificial-intelligence/torch_compile/README.html) +- [Introducing the First AMD 1B Language Models: AMD OLMo](https://www.amd.com/en/developer/resources/technical-articles/introducing-the-first-amd-1b-language-model.html) + +## Bias, Risks, and Limitations + +- The models are being released for research purposes only and are not intended for use cases that require high levels of factuality, safety-critical situations, health, or medical applications, generating false information, facilitating toxic conversations. +- Model checkpoints are made accessible without any safety promises. It is crucial for users to conduct comprehensive evaluations and implement safety filtering mechanisms as per their respective use cases. +- It may be possible to prompt the model to generate content that may be factually inaccurate, harmful, violent, toxic, biased, or otherwise objectionable. Such content may also get generated by prompts that did not intend to produce output as such. Users are thus requested to be aware of this and exercise caution and responsible thinking when using the model. +- Multi-lingual abilities of the models have not been tested and thus may misunderstand and generate erroneous responses across different languages. + +## License + +- The Instella-3B models are licensed for academic and research purposes under a ResearchRAIL license. +- The [amd/Instella-GSM8K-synthetic](https://huggingface.co/datasets/amd/Instella-GSM8K-synthetic) dataset used in second stage pre-training is built with Qwen2.5-72B-Instruct, and is licensed for academic and research purposes under a ResearchRAIL license. Refer to the [LICENSE](https://huggingface.co/datasets/amd/Instella-GSM8K-synthetic/blob/main/LICENSE) and [NOTICES](https://huggingface.co/datasets/amd/Instella-GSM8K-synthetic/blob/main/NOTICES) in the [amd/Instella-GSM8K-synthetic](https://huggingface.co/datasets/amd/Instella-GSM8K-synthetic) dataset card files for more information. +- Refer to the [LICENSE](https://huggingface.co/amd/Instella-3B/blob/main/LICENSE) and [NOTICES](https://huggingface.co/amd/Instella-3B/blob/main/NOTICES) files for more information. + +## Citations + +Feel free to cite our Instella-3B models: + +```text +@misc{Instella, + title = {Instella: Fully Open Language Models with Stellar Performance}, + url = {https://huggingface.co/amd/Instella-3B}, + author = {Jiang Liu, Jialian Wu, Xiaodong Yu, Prakamya Mishra, Sudhanshu Ranjan, Zicheng Liu, Chaitanya Manem, Yusheng Su, Pratik Prabhanjan Brahma, Gowtham Ramesh, Ximeng Sun, Ze Wang, Emad Barsoum}, + month = {March}, + year = {2025} +} +``` \ No newline at 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AttentionMaskConverter +from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast +from transformers.modeling_utils import PreTrainedModel +from transformers.utils import ( + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_flash_attn_2_available, + is_flash_attn_greater_or_equal_2_10, + logging, + replace_return_docstrings, +) + +""" +Instella configuration +""" + +from transformers import AutoConfig, PretrainedConfig + +class InstellaConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`Instella2Model`]. It is used to instantiate an Instella2 + model according to the specified arguments, defining the model architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + + Args: + vocab_size (`int`, *optional*, defaults to 50304): + Vocabulary size of the Instella2 model. Defines the number of different tokens that can be represented by the + `inputs_ids` passed when calling [`Instella2Model`] + hidden_size (`int`, *optional*, defaults to 4096): + Dimension of the hidden representations. + intermediate_size (`int`, *optional*, defaults to 11008): + Dimension of the MLP representations. + num_hidden_layers (`int`, *optional*, defaults to 32): + Number of hidden layers in the Transformer decoder. + num_attention_heads (`int`, *optional*, defaults to 32): + Number of attention heads for each attention layer in the Transformer decoder. + num_key_value_heads (`int`, *optional*): + This is the number of key_value heads that should be used to implement Grouped Query Attention. If + `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if + `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When + converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed + by meanpooling all the original heads within that group. For more details checkout [this + paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to + `num_attention_heads`. + hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): + The non-linear activation function (function or string) in the decoder. + max_position_embeddings (`int`, *optional*, defaults to 2048): + The maximum sequence length that this model might ever be used with. + initializer_range (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + use_cache (`bool`, *optional*, defaults to `True`): + Whether or not the model should return the last key/values attentions (not used by all models). Only + relevant if `config.is_decoder=True`. + pad_token_id (`int`, *optional*, defaults to 1): + Padding token id. + bos_token_id (`int`, *optional*): + Beginning of stream token id. + eos_token_id (`int`, *optional*, defaults to 50279): + End of stream token id. + tie_word_embeddings (`bool`, *optional*, defaults to `False`): + Whether to tie weight embeddings + rope_theta (`float`, *optional*, defaults to 10000.0): + The base period of the RoPE embeddings. + rope_scaling (`Dict`, *optional*): + Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling + strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is + `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update + `max_position_embeddings` to the expected new maximum. See the following thread for more information on how + these scaling strategies behave: + https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an + experimental feature, subject to breaking API changes in future versions. + attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`): + Whether to use a bias in the query, key, value and output projection layers during self-attention. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + rms_norm_eps (`float`, *optional*, defaults to 1e-05): + The epsilon used by the rms normalization layers. + + ```python + >>> from transformers import Instella2Model, Instella2Config + + >>> configuration = Instella2Config() + >>> model = Instella2Model(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "instella" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + vocab_size=50304, + hidden_size=4096, + intermediate_size=11008, + num_hidden_layers=32, + num_attention_heads=32, + num_key_value_heads=None, + hidden_act="silu", + max_position_embeddings=2048, + initializer_range=0.02, + use_cache=True, + pad_token_id=1, + bos_token_id=None, + eos_token_id=50279, + tie_word_embeddings=False, + rope_theta=10000.0, + rope_scaling=None, + attention_bias=False, + attention_dropout=0.0, + rms_norm_eps=1e-5, + **kwargs, + ): + super().__init__( + pad_token_id=pad_token_id, + bos_token_id=bos_token_id, + eos_token_id=eos_token_id, + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) + self.vocab_size = vocab_size + self.max_position_embeddings = max_position_embeddings + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + + # for backward compatibility + if num_key_value_heads is None: + num_key_value_heads = num_attention_heads + + self.num_key_value_heads = num_key_value_heads + self.hidden_act = hidden_act + self.initializer_range = initializer_range + self.use_cache = use_cache + self.rope_theta = rope_theta + self.rope_scaling = rope_scaling + self._rope_scaling_validation() + self.attention_bias = attention_bias + self.attention_dropout = attention_dropout + + self.rms_norm_eps = rms_norm_eps + + def _rope_scaling_validation(self): + """ + Validate the `rope_scaling` configuration. + """ + if self.rope_scaling is None: + return + + if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2: + raise ValueError( + "`rope_scaling` must be a dictionary with two fields, `type` and `factor`, " f"got {self.rope_scaling}" + ) + rope_scaling_type = self.rope_scaling.get("type", None) + rope_scaling_factor = self.rope_scaling.get("factor", None) + if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]: + raise ValueError( + f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" + ) + if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0: + raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}") + + +if is_flash_attn_2_available(): + from transformers.modeling_flash_attention_utils import _flash_attention_forward + + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "InstellaConfig" + + +class InstellaRMSNorm(nn.Module): + def __init__(self, hidden_size, eps=1e-6): + """ + InstellaRMSNorm is equivalent to T5LayerNorm + """ + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon) + return self.weight * hidden_states.to(input_dtype) + + def extra_repr(self): + return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}" + + +# copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Instella +# TODO(joao): add me back asap :) +class InstellaRotaryEmbedding(nn.Module): + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): + super().__init__() + self.scaling_factor = scaling_factor + self.dim = dim + self.max_position_embeddings = max_position_embeddings + self.base = base + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim)) + self.register_buffer("inv_freq", inv_freq, persistent=False) + # For BC we register cos and sin cached + self.max_seq_len_cached = max_position_embeddings + + @torch.no_grad() + def forward(self, x, position_ids): + # x: [bs, num_attention_heads, seq_len, head_size] + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) + position_ids_expanded = position_ids[:, None, :].float() + # Force float32 since bfloat16 loses precision on long contexts + # See https://github.com/huggingface/transformers/pull/29285 + device_type = x.device.type + device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): + freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos = emb.cos() + sin = emb.sin() + return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) + + +# copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Instella +# TODO(joao): add me back asap :) +class InstellaLinearScalingRotaryEmbedding(InstellaRotaryEmbedding): + """InstellaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev""" + + def forward(self, x, position_ids): + # difference to the original RoPE: a scaling factor is aplied to the position ids + position_ids = position_ids.float() / self.scaling_factor + cos, sin = super().forward(x, position_ids) + return cos, sin + + +# copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Instella +# TODO(joao): add me back asap :) +class InstellaDynamicNTKScalingRotaryEmbedding(InstellaRotaryEmbedding): + """InstellaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla""" + + def forward(self, x, position_ids): + # difference to the original RoPE: inv_freq is recomputed when the sequence length > original length + seq_len = torch.max(position_ids) + 1 + if seq_len > self.max_position_embeddings: + base = self.base * ( + (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1) + ) ** (self.dim / (self.dim - 2)) + inv_freq = 1.0 / ( + base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(x.device) / self.dim) + ) + self.register_buffer("inv_freq", inv_freq, persistent=False) # TODO joao: this may break with compilation + + cos, sin = super().forward(x, position_ids) + return cos, sin + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + position_ids (`torch.Tensor`, *optional*): + Deprecated and unused. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +class InstellaAttention(nn.Module): + """Multi-headed attention from 'Attention Is All You Need' paper""" + + # copied from transformers.models.llama.modeling_llama.LlamaAttention.__init__ with Llama->Instella + # TODO(joao): add me back asap :) + def __init__(self, config: InstellaConfig, layer_idx: Optional[int] = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + if layer_idx is None: + logger.warning_once( + f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " + "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " + "when creating this class." + ) + + self.attention_dropout = config.attention_dropout + self.hidden_size = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.max_position_embeddings = config.max_position_embeddings + self.rope_theta = config.rope_theta + self.is_causal = True + + if (self.head_dim * self.num_heads) != self.hidden_size: + raise ValueError( + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" + f" and `num_heads`: {self.num_heads})." + ) + + self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias) + self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias) + self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias) + self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.attention_bias) + self._init_rope() + self.q_norm = InstellaRMSNorm(self.num_heads * self.head_dim, config.rms_norm_eps) + self.k_norm = InstellaRMSNorm(self.num_key_value_heads * self.head_dim, config.rms_norm_eps) + + def _init_rope(self): + if self.config.rope_scaling is None: + self.rotary_emb = InstellaRotaryEmbedding( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + base=self.rope_theta, + ) + else: + scaling_type = self.config.rope_scaling["type"] + scaling_factor = self.config.rope_scaling["factor"] + if scaling_type == "linear": + self.rotary_emb = InstellaLinearScalingRotaryEmbedding( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + scaling_factor=scaling_factor, + base=self.rope_theta, + ) + elif scaling_type == "dynamic": + self.rotary_emb = InstellaDynamicNTKScalingRotaryEmbedding( + self.head_dim, + max_position_embeddings=self.max_position_embeddings, + scaling_factor=scaling_factor, + base=self.rope_theta, + ) + else: + raise ValueError(f"Unknown RoPE scaling type {scaling_type}") + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + cache_position: Optional[torch.LongTensor] = None, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_norm(self.q_proj(hidden_states)) + key_states = self.k_norm(self.k_proj(hidden_states)) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + cos, sin = self.rotary_emb(value_states, position_ids) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_value is not None: + # sin and cos are specific to RoPE models; cache_position needed for the static cache + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attention_mask is not None: # no matter the length, we just slice it + causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] + attn_weights = attn_weights + causal_mask + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class InstellaFlashAttention2(InstellaAttention): + """ + Instella flash attention module. This module inherits from `InstellaAttention` as the weights of the module stays + untouched. The only required change would be on the forward pass where it needs to correctly call the public API of + flash attention and deal with padding tokens in case the input contains any of them. + + Instella flash attention module. This module inherits from `InstellaAttention` as the weights of the module stays + untouched. The only required change would be on the forward pass where it needs to correctly call the public API of + flash attention and deal with padding tokens in case the input contains any of them. + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1. + # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0. + # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left). + self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.LongTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + cache_position: Optional[torch.LongTensor] = None, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + output_attentions = False + + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_norm(self.q_proj(hidden_states)) + key_states = self.k_norm(self.k_proj(hidden_states)) + value_states = self.v_proj(hidden_states) + + # Flash attention requires the input to have the shape + # batch_size x seq_length x head_dim x hidden_dim + # therefore we just need to keep the original shape + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + cos, sin = self.rotary_emb(value_states, position_ids) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_value is not None: + # sin and cos are specific to RoPE models; cache_position needed for the static cache + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache + # to be able to avoid many of these transpose/reshape/view. + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + dropout_rate = self.attention_dropout if self.training else 0.0 + + # In PEFT, usually we cast the layer norms in float32 for training stability reasons + # therefore the input hidden states gets silently casted in float32. Hence, we need + # cast them back in the correct dtype just to be sure everything works as expected. + # This might slowdown training & inference so it is recommended to not cast the LayerNorms + # in fp32. (InstellaRMSNorm handles it correctly) + + input_dtype = query_states.dtype + if input_dtype == torch.float32: + if torch.is_autocast_enabled(): + target_dtype = torch.get_autocast_gpu_dtype() + # Handle the case where the model is quantized + elif hasattr(self.config, "_pre_quantization_dtype"): + target_dtype = self.config._pre_quantization_dtype + else: + target_dtype = self.q_proj.weight.dtype + + logger.warning_once( + f"The input hidden states seems to be silently casted in float32, this might be related to" + f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" + f" {target_dtype}." + ) + + query_states = query_states.to(target_dtype) + key_states = key_states.to(target_dtype) + value_states = value_states.to(target_dtype) + + attn_output = _flash_attention_forward( + query_states, + key_states, + value_states, + attention_mask, + q_len, + position_ids=position_ids, + dropout=dropout_rate, + use_top_left_mask=self._flash_attn_uses_top_left_mask, + is_causal=self.is_causal, + ) + + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() + attn_output = self.o_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class InstellaSdpaAttention(InstellaAttention): + """ + Instella attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from + `InstellaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to + SDPA API. + """ + + # Adapted from InstellaAttention.forward + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: bool = False, + use_cache: bool = False, + cache_position: Optional[torch.LongTensor] = None, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if output_attentions: + # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented. + logger.warning_once( + "InstellaModel is using InstellaSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, " + 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' + ) + return super().forward( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + ) + bsz, q_len, _ = hidden_states.size() + query_states = self.q_norm(self.q_proj(hidden_states)) + key_states = self.k_norm(self.k_proj(hidden_states)) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + cos, sin = self.rotary_emb(value_states, position_ids) + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + if past_key_value is not None: + # sin and cos are specific to RoPE models; cache_position needed for the static cache + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + causal_mask = attention_mask + # if attention_mask is not None and cache_position is not None: + if attention_mask is not None: + causal_mask = causal_mask[:, :, :, : key_states.shape[-2]] + # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask, + # Reference: https://github.com/pytorch/pytorch/issues/112577. + if query_states.device.type == "cuda" and causal_mask is not None: + query_states = query_states.contiguous() + key_states = key_states.contiguous() + value_states = value_states.contiguous() + # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment + # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling. + is_causal = True if causal_mask is None and q_len > 1 else False + attn_output = torch.nn.functional.scaled_dot_product_attention( + query_states, + key_states, + value_states, + attn_mask=causal_mask, + dropout_p=self.attention_dropout if self.training else 0.0, + is_causal=is_causal, + ) + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.view(bsz, q_len, self.hidden_size) + attn_output = self.o_proj(attn_output) + return attn_output, None, past_key_value + + +class InstellaMLP(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.hidden_size = config.hidden_size + self.intermediate_size = config.intermediate_size + self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False) + self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False) + self.act_fn = ACT2FN[config.hidden_act] + + def forward(self, x): + return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) + + +Instella_ATTENTION_CLASSES = { + "eager": InstellaAttention, + "flash_attention_2": InstellaFlashAttention2, + "sdpa": InstellaSdpaAttention, +} + + +class InstellaDecoderLayer(nn.Module): + def __init__(self, config: InstellaConfig, layer_idx: int): + super().__init__() + self.hidden_size = config.hidden_size + + self.self_attn = Instella_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx) + + self.mlp = InstellaMLP(config) + self.pre_attention_layernorm = InstellaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.pre_feedforward_layernorm = InstellaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + # copied from transformers.models.llama.modeling_llama.LlamaDecoderLayer.forward + # TODO(joao): add me back asap :) + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, + **kwargs, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`, *optional*): + attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1, + query_sequence_length, key_sequence_length)` if default attention is used. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding + (see `past_key_values`). + past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states + cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): + Indices depicting the position of the input sequence tokens in the sequence + kwargs (`dict`, *optional*): + Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code + into the model + """ + residual = hidden_states + + # Self Attention + hidden_states = self.pre_attention_layernorm(hidden_states) + hidden_states, self_attn_weights, present_key_value = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + **kwargs, + ) + # hidden_states = self.post_attention_layernorm(hidden_states) + hidden_states = residual + hidden_states + # print(hidden_states) + + # Fully Connected + residual = hidden_states + hidden_states = self.pre_feedforward_layernorm(hidden_states) + hidden_states = self.mlp(hidden_states) + # hidden_states = self.post_feedforward_layernorm(hidden_states) + hidden_states = residual + hidden_states + # print(hidden_states) + + outputs = (hidden_states,) + if output_attentions: + outputs += (self_attn_weights,) + if use_cache: + outputs += (present_key_value,) + return outputs + + +Instella_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`InstellaConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +@add_start_docstrings( + "The bare Instella Model outputting raw hidden-states without any specific head on top.", + Instella_START_DOCSTRING, +) +class InstellaPreTrainedModel(PreTrainedModel): + config_class = InstellaConfig + base_model_prefix = "model" + supports_gradient_checkpointing = True + _no_split_modules = ["InstellaDecoderLayer"] + _skip_keys_device_placement = ["past_key_values"] + _supports_flash_attn_2 = True + _supports_sdpa = True + _supports_cache_class = True + _supports_quantized_cache = True + _supports_static_cache = True + + def _init_weights(self, module): + std = self.config.initializer_range + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + +Instella_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): + Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide + it. + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + [What are input IDs?](../glossary#input-ids) + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + + Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and + [`PreTrainedTokenizer.__call__`] for details. + + If `past_key_values` is used, optionally only the last `input_ids` have to be input (see + `past_key_values`). + + If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] + and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more + information on the default strategy. + + - 1 indicates the head is **not masked**, + - 0 indicates the head is **masked**. + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.n_positions - 1]`. + + [What are position IDs?](../glossary#position-ids) + past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): + Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention + blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` + returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. + + Two formats are allowed: + - a [`~cache_utils.Cache`] instance, see our + [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache); + - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of + shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy + cache format. + + The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the + legacy cache format will be returned. + + If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't + have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` + of shape `(batch_size, sequence_length)`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This + is useful if you want more control over how to convert `input_ids` indices into associated vectors than the + model's internal embedding lookup matrix. + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see + `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): + Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, + this tensor is not affected by padding. It is used to update the cache in the correct position and to infer + the complete sequence length. +""" + + +@add_start_docstrings( + "The bare Instella Model outputting raw hidden-states without any specific head on top.", + Instella_START_DOCSTRING, +) +class InstellaModel(InstellaPreTrainedModel): + """ + Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InstellaDecoderLayer`] + + Args: + config: InstellaConfig + """ + + def __init__(self, config: InstellaConfig): + super().__init__(config) + self.padding_idx = config.pad_token_id + self.vocab_size = config.vocab_size + + self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) + self.layers = nn.ModuleList( + [InstellaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)] + ) + # self.layers = self.layers[:5] + self.norm = InstellaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.gradient_checkpointing = False + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.embed_tokens + + def set_input_embeddings(self, value): + self.embed_tokens = value + + @add_start_docstrings_to_model_forward(Instella_INPUTS_DOCSTRING) + # copied from transformers.models.llama.modeling_llama.LlamaModel.forward + # TODO(joao): add me back asap :) + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + ) -> Union[Tuple, BaseModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if (input_ids is None) ^ (inputs_embeds is not None): + raise ValueError("You must specify exactly one of input_ids or inputs_embeds") + + if self.gradient_checkpointing and self.training and use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`." + ) + use_cache = False + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + # print(inputs_embeds) + + # kept for BC (non `Cache` `past_key_values` inputs) + return_legacy_cache = False + if use_cache and not isinstance(past_key_values, Cache): + return_legacy_cache = True + if past_key_values is None: + past_key_values = DynamicCache() + else: + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + logger.warning_once( + "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and " + "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class " + "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)" + ) + + if cache_position is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + cache_position = torch.arange( + past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device + ) + if position_ids is None: + position_ids = cache_position.unsqueeze(0) + + causal_mask = self._update_causal_mask( + attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions + ) + + # embed positions + hidden_states = inputs_embeds + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = None + + for decoder_layer in self.layers: + if output_hidden_states: + all_hidden_states += (hidden_states,) + + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + causal_mask, + position_ids, + past_key_values, + output_attentions, + use_cache, + cache_position, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=causal_mask, + position_ids=position_ids, + past_key_value=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + ) + + hidden_states = layer_outputs[0] + + if use_cache: + next_decoder_cache = layer_outputs[2 if output_attentions else 1] + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + hidden_states = self.norm(hidden_states) + # print(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = next_decoder_cache if use_cache else None + if return_legacy_cache: + next_cache = next_cache.to_legacy_cache() + + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + + def _update_causal_mask( + self, + attention_mask: torch.Tensor, + input_tensor: torch.Tensor, + cache_position: torch.Tensor, + past_key_values: Cache, + output_attentions: bool, + ): + if self.config._attn_implementation == "flash_attention_2": + if attention_mask is not None and 0.0 in attention_mask: + return attention_mask + return None + + # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in + # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail + # to infer the attention mask. + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + using_static_cache = isinstance(past_key_values, StaticCache) + + # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward + if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions: + if AttentionMaskConverter._ignore_causal_mask_sdpa( + attention_mask, + inputs_embeds=input_tensor, + past_key_values_length=past_seen_tokens, + is_training=self.training, + ): + return None + + dtype, device = input_tensor.dtype, input_tensor.device + sequence_length = input_tensor.shape[1] + if using_static_cache: + target_length = past_key_values.get_max_cache_shape() + else: + target_length = ( + attention_mask.shape[-1] + if isinstance(attention_mask, torch.Tensor) + else past_seen_tokens + sequence_length + 1 + ) + + # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). + causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position( + attention_mask, + sequence_length=sequence_length, + target_length=target_length, + dtype=dtype, + device=device, + cache_position=cache_position, + batch_size=input_tensor.shape[0], + ) + + if ( + self.config._attn_implementation == "sdpa" + and attention_mask is not None + and attention_mask.device.type == "cuda" + and not output_attentions + ): + # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when + # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. + # Details: https://github.com/pytorch/pytorch/issues/110213 + min_dtype = torch.finfo(dtype).min + causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) + + return causal_mask + + @staticmethod + def _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask: torch.Tensor, + sequence_length: int, + target_length: int, + dtype: torch.dtype, + device: torch.device, + cache_position: torch.Tensor, + batch_size: int, + **kwargs, + ): + """ + Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape + `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. + + Args: + attention_mask (`torch.Tensor`): + A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape + `(batch_size, 1, query_length, key_value_length)`. + sequence_length (`int`): + The sequence length being processed. + target_length (`int`): + The target length: when generating with static cache, the mask should be as long as the static cache, + to account for the 0 padding, the part of the cache that is not filled yet. + dtype (`torch.dtype`): + The dtype to use for the 4D attention mask. + device (`torch.device`): + The device to plcae the 4D attention mask on. + cache_position (`torch.Tensor`): + Indices depicting the position of the input sequence tokens in the sequence. + batch_size (`torch.Tensor`): + Batch size. + """ + if attention_mask is not None and attention_mask.dim() == 4: + # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. + causal_mask = attention_mask + else: + min_dtype = torch.finfo(dtype).min + causal_mask = torch.full( + (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device + ) + if sequence_length != 1: + causal_mask = torch.triu(causal_mask, diagonal=1) + causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) + causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) + if attention_mask is not None: + causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit + mask_length = attention_mask.shape[-1] + padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :] + padding_mask = padding_mask == 0 + causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( + padding_mask, min_dtype + ) + + return causal_mask + +# TODO: re-enable check: Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM with LLAMA->Instella,Llama->Instella +class InstellaForCausalLM(InstellaPreTrainedModel, GenerationMixin): + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config: InstellaConfig): + super().__init__(config) + self.model = InstellaModel(config) + self.vocab_size = config.vocab_size + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.model.embed_tokens + + def set_input_embeddings(self, value): + self.model.embed_tokens = value + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + def set_decoder(self, decoder): + self.model = decoder + + def get_decoder(self): + return self.model + + @add_start_docstrings_to_model_forward(Instella_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) + # Ignore copy + def forward( + self, + input_ids: torch.LongTensor = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[List[torch.FloatTensor]] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[torch.LongTensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + num_logits_to_keep: int = 0, + **loss_kwargs, + ) -> Union[Tuple, CausalLMOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., + config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored + (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. + + num_logits_to_keep (`int`, *optional*): + Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all + `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that + token can save memory, which becomes pretty significant for long sequences or large vocabulary size. + + Returns: + + Example: + + ```python + >>> from transformers import AutoTokenizer, InstellaForCausalLM + + >>> model = InstellaForCausalLM.from_pretrained("allenai/Instella2-1B-hf") + >>> tokenizer = AutoTokenizer.from_pretrained("allenai/Instella2-1B-hf") + + >>> prompt = "Hey, are you conscious? Can you talk to me?" + >>> inputs = tokenizer(prompt, return_tensors="pt") + + >>> # Generate + >>> generate_ids = model.generate(inputs.input_ids, max_length=30) + >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] + 'Hey, are you conscious? Can you talk to me?\nI’m not sure if you’re conscious of this, but I’m' + ``` + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + cache_position=cache_position, + ) + + hidden_states = outputs[0] + # Only compute necessary logits, and do not upcast them to float if we are not computing the loss + logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]) + + loss = None + if labels is not None: + loss = self.loss_function(logits, labels, self.vocab_size, **loss_kwargs) + + if not return_dict: + output = (logits,) + outputs[1:] + return (loss,) + output if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=logits, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) + diff --git a/scaling_perf_instruct.png b/scaling_perf_instruct.png new file mode 100644 index 0000000..fe839cf --- /dev/null +++ b/scaling_perf_instruct.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a4656f6e79da15d78cc7c02d095ff91cda3c7c22629a824fdf12892c3a37ea3c +size 504425 diff --git a/special_tokens_map.json b/special_tokens_map.json new file mode 100644 index 0000000..fa7c9d1 --- /dev/null +++ b/special_tokens_map.json @@ -0,0 +1,24 @@ +{ + "bos_token": { + "content": "<|endoftext|>", 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12820, + "Ġpregnant": 12821, + "Ms": 12822, + "Ġcomputers": 12823, + "ĠEdward": 12824, + "Ġtoxic": 12825, + "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ": 12826, + "Equal": 12827, + "ĠMah": 12828, + "ĠLy": 12829, + "Ġhits": 12830, + "Ġshield": 12831, + "Ġliterally": 12832, + "Ġenthusi": 12833, + "Ġdefects": 12834, + "Ġfucking": 12835, + "âĢħ": 12836, + "empl": 12837, + "ĠLE": 12838, + "Ġcaps": 12839, + "berry": 12840, + "Ġignored": 12841, + "Ġ128": 12842, + "ĠPrince": 12843, + "acher": 12844, + "Ġutilized": 12845, + "regular": 12846, + "bc": 12847, + "Ġvaried": 12848, + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ": 12849, + "norm": 12850, + "Ġownership": 12851, + "109": 12852, + "Ġdenotes": 12853, + "acco": 12854, + "vir": 12855, + "hom": 12856, + "Sche": 12857, + "Ġchamp": 12858, + "×ķ×": 12859, + "Ġheaded": 12860, + "Ġdeeper": 12861, + "ocols": 12862, + "arts": 12863, + "Take": 12864, + "Ġly": 12865, + "cases": 12866, + "ĠFoot": 12867, + "Ġfilling": 12868, + "Ġroy": 12869, + "Ġescap": 12870, + "Bit": 12871, + 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"Ġneighb": 12924, + "Ġguidance": 12925, + "Ġgravity": 12926, + "Ġocean": 12927, + "params": 12928, + "nex": 12929, + "Ġdescribing": 12930, + "ì": 12931, + "ĠWin": 12932, + "ĠSav": 12933, + "Ġintens": 12934, + "ogram": 12935, + "Ġalongside": 12936, + "isl": 12937, + "ĠSR": 12938, + "Ġchat": 12939, + "Ġisolation": 12940, + "Ġvisits": 12941, + "Ġvisitors": 12942, + "ounce": 12943, + "SV": 12944, + "}$$\\": 12945, + "Ġinvestors": 12946, + "inners": 12947, + "Ġburst": 12948, + "Ġmini": 12949, + "ettings": 12950, + "ĠNic": 12951, + "Ġoccurring": 12952, + "Ġnamespace": 12953, + "Ġworker": 12954, + "Ġvariant": 12955, + "Ġadapted": 12956, + "Ġreveals": 12957, + "Ġguests": 12958, + "Ġmemories": 12959, + "Ġregistration": 12960, + "ĠBudd": 12961, + "ĠMas": 12962, + "ĠBra": 12963, + "fc": 12964, + "Run": 12965, + "Ġcub": 12966, + "ĠWord": 12967, + "ĠMap": 12968, + "Ġruled": 12969, + "Ġoverride": 12970, + "106": 12971, + "ĠNight": 12972, + "lower": 12973, + "Ġintegrity": 12974, + "holders": 12975, + 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"pow": 13029, + "Main": 13030, + "Ġmock": 13031, + "Ġentrance": 13032, + "ĠÃIJ": 13033, + "1994": 13034, + "Ġcrash": 13035, + "ĠPrior": 13036, + "Ġgathered": 13037, + "missible": 13038, + "ĠLear": 13039, + "ĠThom": 13040, + "rb": 13041, + "Ġlys": 13042, + "оÑģ": 13043, + "Ġthrown": 13044, + "RES": 13045, + "agg": 13046, + "endor": 13047, + "Ġinches": 13048, + "thur": 13049, + "Ġfeas": 13050, + "ĠAnderson": 13051, + "Ġlikes": 13052, + "Ġlum": 13053, + "ĠLewis": 13054, + "burgh": 13055, + "Ġmonthly": 13056, + "edge": 13057, + "====================": 13058, + "Ġargs": 13059, + "watch": 13060, + "_{(": 13061, + "Ġ103": 13062, + "ĠAz": 13063, + "Ġdimin": 13064, + "Ġni": 13065, + "Ġcann": 13066, + "Ġdefines": 13067, + "lab": 13068, + "Å£": 13069, + "help": 13070, + "Ġperception": 13071, + "Ġdeclined": 13072, + "arded": 13073, + "total": 13074, + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ": 13075, + "ĨĴ": 13076, + "utt": 13077, + "aton": 13078, + "Ġhopes": 13079, + "ие": 13080, + "Ġolig": 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"ailing": 13454, + "Ġprolong": 13455, + "sky": 13456, + "Connection": 13457, + "Ġpointing": 13458, + "pany": 13459, + "Ġva": 13460, + "Ġweights": 13461, + "achusetts": 13462, + "Ġovernight": 13463, + "ockey": 13464, + "Ġfought": 13465, + "Ġphenotype": 13466, + "elect": 13467, + "omorphic": 13468, + "Ġeliminate": 13469, + "ĠPlace": 13470, + "Ġbreaks": 13471, + "derived": 13472, + "tor": 13473, + "Ġexport": 13474, + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ": 13475, + "Ġlucky": 13476, + "Ġconfused": 13477, + "ĠImages": 13478, + "Ġsensors": 13479, + "ĠRail": 13480, + "replace": 13481, + "129": 13482, + "Ġquantities": 13483, + "人": 13484, + "images": 13485, + "åĩ": 13486, + "Ġsquad": 13487, + "$]{}": 13488, + "ĠCreat": 13489, + "ativ": 13490, + "Ġlovely": 13491, + "ĠLinux": 13492, + "Ġmaximal": 13493, + "requency": 13494, + "olecular": 13495, + "Ġequipped": 13496, + "Ġpainting": 13497, + "Ġplatforms": 13498, + "éĩ": 13499, + "ĠAdministration": 13500, + "ĠCre": 13501, + "Enc": 13502, + "Ġcomplement": 13503, + "ĠÙħ": 13504, + "zes": 13505, + "Ġsynthetic": 13506, + "ĠEC": 13507, + "utable": 13508, + "elcome": 13509, + "Custom": 13510, + "oj": 13511, + "Ġseats": 13512, + "Ġvoor": 13513, + "yard": 13514, + "Ġshortly": 13515, + "ĠCome": 13516, + "domain": 13517, + "meta": 13518, + "Ġcourses": 13519, + "Ġubuntu": 13520, + "Ġprogramme": 13521, + ":=": 13522, + "Ċĉĉĉĉĉĉĉĉ": 13523, + "three": 13524, + "ĠFull": 13525, + "âĶĢâĶĢ": 13526, + "Ġregulated": 13527, + "ĠCF": 13528, + "ĠNothing": 13529, + "parison": 13530, + "ĠNorm": 13531, + "Ġfitting": 13532, + "ĠToronto": 13533, + "ĠBible": 13534, + "idi": 13535, + "ĠAdv": 13536, + "ressing": 13537, + "Ġpreventing": 13538, + "ashes": 13539, + "ioxid": 13540, + "ĠMom": 13541, + "Ġmanually": 13542, + "ulent": 13543, + ">\\": 13544, + "ĠPrinc": 13545, + "Ġobtaining": 13546, + "Ġfallen": 13547, + "Ġdop": 13548, + "1993": 13549, + "court": 13550, + "Ġcollapse": 13551, + "priv": 13552, + "Ġcombinations": 13553, + "Ġtrends": 13554, + 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"ĠWade": 31678, + "edar": 31679, + "Ġprogenitor": 31680, + "ĠNSString": 31681, + "ĠÑĨ": 31682, + "ĠOz": 31683, + "Ġdestinations": 31684, + "BMI": 31685, + "ideos": 31686, + "Ġ1927": 31687, + "Ġslit": 31688, + "iliated": 31689, + "Ġmigrate": 31690, + "Ġminorities": 31691, + "ĠSummit": 31692, + "Ġleaks": 31693, + "SRC": 31694, + "agul": 31695, + "Forward": 31696, + "Ġpdf": 31697, + "ãģĵãģ®": 31698, + "476": 31699, + "CONST": 31700, + "Ġremission": 31701, + "Ġchasing": 31702, + "Serializer": 31703, + "ĠWnt": 31704, + "Ġwrapping": 31705, + "ĠBudget": 31706, + "Ġ1910": 31707, + "reactive": 31708, + "ulp": 31709, + "Ġmeteor": 31710, + "District": 31711, + "ãĥĩ": 31712, + "Ġ\\**": 31713, + "Ġsticky": 31714, + "erton": 31715, + "ĠHI": 31716, + "ĠAlexa": 31717, + "ilated": 31718, + "ĠRNAs": 31719, + "Ġtattoo": 31720, + "ĠShir": 31721, + "955": 31722, + "achines": 31723, + "IDs": 31724, + "atican": 31725, + "ĠCord": 31726, + "Large": 31727, + "hc": 31728, + "riminal": 31729, + "643": 31730, + 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"Validation": 31780, + "Ġplague": 31781, + "äº": 31782, + "diag": 31783, + "Ñĥм": 31784, + "rivers": 31785, + "Ġfamil": 31786, + "ĠDy": 31787, + "Ġnicotine": 31788, + "Ġ[...]": 31789, + "ĠEns": 31790, + "Ġcries": 31791, + "oslav": 31792, + "Ġregularity": 31793, + "OA": 31794, + "Ġhav": 31795, + "Appendix": 31796, + "otta": 31797, + "Ġcautious": 31798, + "ĠDynamic": 31799, + "Ġslammed": 31800, + "Ġcamb": 31801, + "ĠViv": 31802, + "bben": 31803, + "Ġny": 31804, + "ĠPeru": 31805, + "Ġburnt": 31806, + "')$": 31807, + "indent": 31808, + "asms": 31809, + "hydrox": 31810, + "è·": 31811, + "Ġisso": 31812, + "orporated": 31813, + "ĠFoo": 31814, + "belief": 31815, + "athing": 31816, + "Ġdrained": 31817, + "Ñħод": 31818, + "478": 31819, + "ĠÐIJ": 31820, + "erably": 31821, + "ĠTiger": 31822, + "XL": 31823, + "Ġdwelling": 31824, + "Ġannotations": 31825, + "Pair": 31826, + "TEGER": 31827, + "Ġàª": 31828, + "Ġwagon": 31829, + ".\"[": 31830, + "Ġatop": 31831, + "Ġretard": 31832, + "Ġdamned": 31833, + 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"stellar": 31885, + "Loading": 31886, + "Flor": 31887, + "Ġbounce": 31888, + "Wire": 31889, + "ĠWals": 31890, + "benef": 31891, + "ydr": 31892, + "fitting": 31893, + "Ġfried": 31894, + "åł´": 31895, + "Ġdelegation": 31896, + "ĠLopez": 31897, + "ceptive": 31898, + "\"|": 31899, + "Marc": 31900, + "Ġintends": 31901, + "åĿ": 31902, + "ĠManuel": 31903, + "Ġ\\*\\*\\*": 31904, + "Ġenthusiastic": 31905, + "ameth": 31906, + "Ġteammates": 31907, + "Ġethnicity": 31908, + "ĠSOC": 31909, + "ĠLay": 31910, + "omonas": 31911, + "ëĭĪëĭ¤": 31912, + "ĠAntar": 31913, + "ĠMilwaukee": 31914, + "Ġcontempor": 31915, + "ĠTitan": 31916, + "Ġsadness": 31917, + "Ġtiene": 31918, + "Ġquint": 31919, + "Ġsquamous": 31920, + "Ġfuzzy": 31921, + "XT": 31922, + "esti": 31923, + "Ġclan": 31924, + "Ġexcellence": 31925, + "Ġfootballer": 31926, + "ĠSig": 31927, + "Ġaños": 31928, + "Ġindist": 31929, + "extend": 31930, + "Ġabbrev": 31931, + "xC": 31932, + "ĠRush": 31933, + "Ġmd": 31934, + "ĠInstitutes": 31935, + "Ġlamin": 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"ĠĠĠĠĊĠ": 31988, + "ĠÐĶ": 31989, + "ĠKitchen": 31990, + "Ġpains": 31991, + "Ġcanon": 31992, + "ĠTEST": 31993, + "Ġlookup": 31994, + "Ġoutgoing": 31995, + "Cover": 31996, + "reon": 31997, + "Ġusable": 31998, + "ĠMats": 31999, + "ĠMunich": 32000, + "culated": 32001, + "Ġ370": 32002, + "Scheme": 32003, + "è¿ĩ": 32004, + "ĠFear": 32005, + "472": 32006, + "unter": 32007, + "ĠSax": 32008, + "mania": 32009, + "Ġcatches": 32010, + "ylate": 32011, + "nail": 32012, + "idepress": 32013, + "Ġintervene": 32014, + "ĠPartnership": 32015, + "ĠFaculty": 32016, + "Ġ׾": 32017, + "ĠPars": 32018, + "Ġlinguistic": 32019, + "è¦ĭ": 32020, + "ĠLIKE": 32021, + "Ġsous": 32022, + "MHz": 32023, + "ĠDAMAGES": 32024, + "REEK": 32025, + "èĤ": 32026, + "Ġrelent": 32027, + "anya": 32028, + "currency": 32029, + "Ġsão": 32030, + "Chen": 32031, + "ĠParameters": 32032, + "Ġtapped": 32033, + "enment": 32034, + "Way": 32035, + "ĠMED": 32036, + "ĠHU": 32037, + "Ġchemok": 32038, + "Ġconfer": 32039, + "Tok": 32040, + "Ġsensed": 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34182, + "Ġfres": 34183, + "ĠWaters": 34184, + "zar": 34185, + "ĠMoss": 34186, + "ClickListener": 34187, + "Ġfixture": 34188, + "escape": 34189, + "Ġtransfusion": 34190, + "Science": 34191, + "ĠCastro": 34192, + "ĠTCR": 34193, + ";&#": 34194, + "oshop": 34195, + "ĠGlobe": 34196, + "ĠApollo": 34197, + "ãĤį": 34198, + "Ġburglary": 34199, + "Claim": 34200, + "escence": 34201, + "Ġembassy": 34202, + "Ġwhip": 34203, + "主": 34204, + "Ġ359": 34205, + "amil": 34206, + "ĠWine": 34207, + "Ġcans": 34208, + "Ġmeaningless": 34209, + "Âŀ": 34210, + "ĠMillion": 34211, + "citations": 34212, + "ĠNed": 34213, + "Poss": 34214, + "ĠSão": 34215, + "Ġeconomists": 34216, + "Ġин": 34217, + "structured": 34218, + "Ġexcel": 34219, + "Ġdiscretionary": 34220, + "ĠElli": 34221, + "Signature": 34222, + "ilda": 34223, + "Ġsuppressor": 34224, + "ĠWC": 34225, + "Ġgarn": 34226, + "})(": 34227, + "Browser": 34228, + "ĠĠĠĠĠĠĠĠĠĠĊĠ": 34229, + "430": 34230, + "asome": 34231, + "Ġupgrades": 34232, + "Ġdoping": 34233, + 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"ĠAH": 40642, + "^),": 40643, + "Modal": 40644, + "Community": 40645, + "Ġdisguise": 40646, + "ĠÏĥε": 40647, + "\"-": 40648, + "Ġdrowned": 40649, + "asionally": 40650, + "difference": 40651, + "Ġpillars": 40652, + "ĠInstrument": 40653, + "areth": 40654, + "Ġdisgrace": 40655, + "Ġchoir": 40656, + "Ġ441": 40657, + "åıĬ": 40658, + "Ġaspirations": 40659, + "Ġbef": 40660, + "Ġshores": 40661, + "Ġprudent": 40662, + "Ġmisunderstanding": 40663, + "Ġquartz": 40664, + "Ġazimuth": 40665, + "515": 40666, + "Ġtents": 40667, + "ATURE": 40668, + ".''": 40669, + "ĠPag": 40670, + "ĠWriter": 40671, + "Ġbland": 40672, + "âĪ¼": 40673, + "Ġsyringe": 40674, + "Prec": 40675, + "Ġcondemnation": 40676, + "Ġpoliceman": 40677, + "Ġconfuse": 40678, + "Ġ:-": 40679, + "LEX": 40680, + "otoxic": 40681, + "Ġalgun": 40682, + "Ġbasil": 40683, + "ĠContainer": 40684, + "iculously": 40685, + "ĠSUN": 40686, + "Ġobserves": 40687, + "ĠInto": 40688, + "Ġwhence": 40689, + "Ġmaterially": 40690, + "terminus": 40691, + "CES": 40692, + "éĤ£": 40693, + "Ġinsisting": 40694, + "Jam": 40695, + "Ġdann": 40696, + "resistance": 40697, + "Ġком": 40698, + "Ġchiefly": 40699, + "Ju": 40700, + "Picture": 40701, + "âĢ¨": 40702, + "ĠDismiss": 40703, + "Ġhugely": 40704, + "bish": 40705, + "ĠTelesc": 40706, + "Ġtownship": 40707, + "ĠTemp": 40708, + "Ġdissolve": 40709, + "Ġmysteries": 40710, + "Ġpuis": 40711, + "phrase": 40712, + "ĠDidn": 40713, + "vier": 40714, + "éħį": 40715, + "Ġbattalion": 40716, + "ĠAgricultural": 40717, + "graphics": 40718, + "shoot": 40719, + "Ġguitarist": 40720, + "ĠHubble": 40721, + "Reason": 40722, + "Ġtheological": 40723, + "Ġeats": 40724, + "Ġexploits": 40725, + "ĠGraves": 40726, + "Ġtutorials": 40727, + "sand": 40728, + "iston": 40729, + "Ġ534": 40730, + "Generate": 40731, + "Ġtrout": 40732, + "ĠIon": 40733, + "Variant": 40734, + "Ġadiab": 40735, + "Ġasymmet": 40736, + "ĠCampus": 40737, + "ĠRoland": 40738, + "SIOC": 40739, + "NEY": 40740, + "Ġsausage": 40741, + "bron": 40742, + "ubotu": 40743, + "econom": 40744, + "ĠExit": 40745, + "Ġreconciliation": 40746, + "uddled": 40747, + "quiet": 40748, + "Ġpatterned": 40749, + "Cherry": 40750, + "ĠBasketball": 40751, + "Ġstratification": 40752, + "ĠCod": 40753, + "Ġchased": 40754, + "Ġautomorphism": 40755, + "urus": 40756, + "Ġcommune": 40757, + "ĠMate": 40758, + "ÃŃo": 40759, + "465": 40760, + "773": 40761, + "Ġinning": 40762, + "Ġgrac": 40763, + "ĠConse": 40764, + "Ġanion": 40765, + "Ġprevailed": 40766, + "ĠBrisbane": 40767, + "ĠEthernet": 40768, + "Ġзап": 40769, + "Ġborrowing": 40770, + "ĠShane": 40771, + "ĠStrick": 40772, + "Kevin": 40773, + "Ġplating": 40774, + "nego": 40775, + "ĠHollow": 40776, + "Ġbrisk": 40777, + "?).": 40778, + "Words": 40779, + "ichlor": 40780, + "vue": 40781, + "ĠFashion": 40782, + "Ġ484": 40783, + "Ġtracts": 40784, + "Ġdoctrines": 40785, + "cans": 40786, + "ĠYugoslav": 40787, + "ĠIndividuals": 40788, + "ĠWD": 40789, + "Ġworthless": 40790, + "Ġonboard": 40791, + "ddot": 40792, + "ĠMae": 40793, + "ĠJoey": 40794, + "Didn": 40795, + "Silver": 40796, + "Ġaprès": 40797, + "Ġpositional": 40798, + "ĠHSV": 40799, + "Ġtras": 40800, + "Arabidopsis": 40801, + "اد": 40802, + "Ġwurde": 40803, + "NUMBER": 40804, + "Anna": 40805, + "Ġshutting": 40806, + "ergio": 40807, + "व": 40808, + "Ġflung": 40809, + "Ġsavage": 40810, + "Ġsalvage": 40811, + "applicable": 40812, + "animate": 40813, + "ĠRid": 40814, + "Ġrodent": 40815, + "ĠStrange": 40816, + "000001": 40817, + "Ġvowed": 40818, + "Ġrand": 40819, + "ierno": 40820, + "Ġhomeowners": 40821, + "Cfg": 40822, + "že": 40823, + "ĠHumph": 40824, + "ĠCoc": 40825, + "abo": 40826, + "rende": 40827, + "ĠKnowing": 40828, + "Ut": 40829, + "ĠREAL": 40830, + "shown": 40831, + "Ġprosecute": 40832, + "venir": 40833, + "Ġbaggage": 40834, + "Ġinland": 40835, + "ogel": 40836, + "ÑĤÑĮ": 40837, + "SEE": 40838, + "ĠChiefs": 40839, + "ĠLabs": 40840, + "Ġlors": 40841, + "ACCESS": 40842, + "é¦": 40843, + "prior": 40844, + "Ġovercoming": 40845, + "RAF": 40846, + "Ġcafe": 40847, + "438": 40848, + "Ko": 40849, + "ĠLN": 40850, + "ADA": 40851, + "ĠSetup": 40852, + "1949": 40853, + "Ġcolitis": 40854, + "Ġrestruct": 40855, + "ĠBachelor": 40856, + "thren": 40857, + "ĠLaden": 40858, + "âģĦ": 40859, + "Ġmidfield": 40860, + "ENG": 40861, + "ĠCorrection": 40862, + "ĠCowboys": 40863, + "ĠNike": 40864, + "Ġbearings": 40865, + "Heat": 40866, + "Ġribosomal": 40867, + "ĠPompe": 40868, + "plicial": 40869, + "indo": 40870, + "Ġgalleries": 40871, + "Ġwaive": 40872, + "Ġ485": 40873, + "ĠInterstate": 40874, + "serve": 40875, + "Ġluminous": 40876, + "Ġcompiling": 40877, + "Ġcontradicts": 40878, + "ĠErnest": 40879, + "Ġdecentralized": 40880, + "Ġfaç": 40881, + "Ġphonon": 40882, + "ourcing": 40883, + "mighty": 40884, + "owler": 40885, + "Ġconcave": 40886, + "iPhone": 40887, + "ruvate": 40888, + "Ġasshole": 40889, + "Ġdecoration": 40890, + "Ġmigrated": 40891, + "ĠCopenhagen": 40892, + "challeng": 40893, + "Ġcovert": 40894, + "ãĥ¼ãĥ«": 40895, + "437": 40896, + "ĠDup": 40897, + "Ġmythology": 40898, + "Ġconcess": 40899, + "ĠExpert": 40900, + "enging": 40901, + "raiser": 40902, + "Ġstarred": 40903, + "--------------------------------------------": 40904, + "ĠBooth": 40905, + "ĠHaiti": 40906, + "numer": 40907, + "Ġradios": 40908, + "^;": 40909, + "Ġcompost": 40910, + "Ġimpending": 40911, + "à§ĩ": 40912, + "ĠØŃ": 40913, + "textsc": 40914, + "ĠTue": 40915, + "innen": 40916, + "Kal": 40917, + "salt": 40918, + "RW": 40919, + "gren": 40920, + "IMP": 40921, + "Ġacquis": 40922, + "Ġ\".\"": 40923, + "Ġexem": 40924, + "Ġengages": 40925, + "Ġmirac": 40926, + "tooltip": 40927, + "!,": 40928, + "Ġleaking": 40929, + "aseous": 40930, + "dala": 40931, + "TING": 40932, + "ancia": 40933, + "ĠRash": 40934, + "Ġreminding": 40935, + "ĠVil": 40936, + "Ġ1903": 40937, + "ĠDogs": 40938, + "outside": 40939, + "Ġlattices": 40940, + "ĠMCP": 40941, + "Apparently": 40942, + "Ġbowls": 40943, + "Ġsubstituting": 40944, + "Gender": 40945, + "akan": 40946, + "ophysical": 40947, + "445": 40948, + "ĠVariables": 40949, + "Rx": 40950, + "äºĽ": 40951, + "coupled": 40952, + "Od": 40953, + "Ġobjet": 40954, + "Ġexpansions": 40955, + "MU": 40956, + "numbers": 40957, + "resis": 40958, + "Ġ513": 40959, + "ĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ": 40960, + "Ġpalette": 40961, + "Ġlantern": 40962, + "Ġcosa": 40963, + "embedded": 40964, + "zw": 40965, + "ĠMagnetic": 40966, + "edges": 40967, + "ĠIsraelis": 40968, + "åĢ¼": 40969, + "vu": 40970, + "ĠNietzsche": 40971, + "ĠChambers": 40972, + "ĊĉĉĠ": 40973, + "Ġmelanch": 40974, + "Ġalarming": 40975, + "ĠпÑĢав": 40976, + "Ġecology": 40977, + "epit": 40978, + "ndt": 40979, + "rÃŃa": 40980, + "Ġdrap": 40981, + "Ġ451": 40982, + "ĠMorocco": 40983, + "Maximum": 40984, + "ĠBom": 40985, + "ĠNZ": 40986, + "ĠDAPI": 40987, + "ophobia": 40988, + "ĠNeo": 40989, + "wagen": 40990, + "073": 40991, + "Ġcardio": 40992, + "ske": 40993, + "estial": 40994, + "wolf": 40995, + "ĠNortheast": 40996, + "vote": 40997, + "ĠClayton": 40998, + "Ġcela": 40999, + "Ġ'[": 41000, + "Ġboo": 41001, + "Ġcolspan": 41002, + "ĠAPC": 41003, + "ĠPEOPLE": 41004, + "Ġfirstly": 41005, + "Ġrespecting": 41006, + "Ġtorped": 41007, + "Ġplayground": 41008, + "ĠMcConnell": 41009, + "contained": 41010, + "destination": 41011, + "Ġreel": 41012, + "Ġappraisal": 41013, + "ÑĢо": 41014, + "åı·": 41015, + "DRV": 41016, + "ĠQuite": 41017, + "\\]\\[": 41018, + "Ġpiles": 41019, + "ĠiPod": 41020, + "Ġwastewater": 41021, + "imedia": 41022, + "Ġscholarly": 41023, + "Ġamused": 41024, + "Ġexponents": 41025, + "Delay": 41026, + "Ġthwart": 41027, + "ĠBalk": 41028, + "Ġ468": 41029, + "Ġdisagreed": 41030, + "Ġuranium": 41031, + "Ger": 41032, + "Ġsuppresses": 41033, + "scalar": 41034, + "Ġhasht": 41035, + "ĠBW": 41036, + "ĠWB": 41037, + "virus": 41038, + "Ġhypertensive": 41039, + "079": 41040, + "Applied": 41041, + "threads": 41042, + "ATIONAL": 41043, + "Ġcelebrations": 41044, + "Ġloot": 41045, + "ĠBreast": 41046, + "imod": 41047, + "Ġslash": 41048, + "Ġcumbersome": 41049, + "orphic": 41050, + "Computer": 41051, + "Sad": 41052, + "ĠHQ": 41053, + "ĠStrike": 41054, + "Ġ547": 41055, + "Ġneutrality": 41056, + "Ġainsi": 41057, + "ĠSprint": 41058, + "ĠNEWS": 41059, + "олÑĮз": 41060, + "Ġacquainted": 41061, + "Ky": 41062, + "ĠFN": 41063, + "enoid": 41064, + "ĠDMEM": 41065, + "Ġwording": 41066, + "afia": 41067, + "Ġantitumor": 41068, + "ĠHBO": 41069, + "Ġglor": 41070, + "reports": 41071, + "Ġcooker": 41072, + "ĠBillboard": 41073, + "Demo": 41074, + "Ġexploratory": 41075, + "Ġawarding": 41076, + "Ġalters": 41077, + "ĠUINT": 41078, + "ĠReligious": 41079, + "esan": 41080, + "561": 41081, + "Ġclergy": 41082, + "1952": 41083, + "Ġcollects": 41084, + "éľĢ": 41085, + "Ðķ": 41086, + "ĠHerman": 41087, + "Ġstealth": 41088, + "Ġzoo": 41089, + "innerHTML": 41090, + "ĠLegacy": 41091, + "UINT": 41092, + "oarth": 41093, + "Cd": 41094, + "Ġpitches": 41095, + "Ġave": 41096, + "ĠHicks": 41097, + "ĠShan": 41098, + "Sounds": 41099, + "ĠChef": 41100, + "ĠComey": 41101, + "ãģĹãģ¦ãģĦãĤĭ": 41102, + "Ġmornings": 41103, + "PLA": 41104, + "ectin": 41105, + "Ġhecho": 41106, + "SEQ": 41107, + "Ġdeputies": 41108, + "ĠDex": 41109, + "CCCC": 41110, + "Ġfrivolous": 41111, + "ĠSterling": 41112, + "Ġbounding": 41113, + "avorable": 41114, + "Ġflowed": 41115, + "Ġseminal": 41116, + "ĠZoo": 41117, + "ĠCorinth": 41118, + "Ġnitride": 41119, + "ĠEsther": 41120, + "oelect": 41121, + "freq": 41122, + "aminergic": 41123, + "Differentiate": 41124, + "Ġthru": 41125, + "ĠReset": 41126, + "GRE": 41127, + "Ġexquisite": 41128, + "Ġbloc": 41129, + "Ġferromagnetic": 41130, + "ĠAmber": 41131, + "à¸ķ": 41132, + "Prime": 41133, + "krit": 41134, + "Ġspecs": 41135, + "ĠQaeda": 41136, + "*~,": 41137, + "Ġtransmissions": 41138, + "Ġthreaded": 41139, + "åŁº": 41140, + "ĠCBC": 41141, + "Ġcontag": 41142, + "Ġpredator": 41143, + "ĠSignaling": 41144, + "Cool": 41145, + "Ġsenescence": 41146, + "ÙĪÙĦ": 41147, + "/%": 41148, + ")',": 41149, + "iceps": 41150, + "ĠMaya": 41151, + "Ġshields": 41152, + "Ġswell": 41153, + "isciplinary": 41154, + "Ġeukaryotic": 41155, + "Ġ544": 41156, + "osecond": 41157, + "ĠCly": 41158, + "Ġintimacy": 41159, + "Yang": 41160, + "dwell": 41161, + "ãģĵãĤĮ": 41162, + "micromachines": 41163, + "Ġdetta": 41164, + "Ġquarant": 41165, + "ĠEmployees": 41166, + "Ġelucidated": 41167, + "Ġnoodles": 41168, + "Certificate": 41169, + "Ġnú": 41170, + "Ġgente": 41171, + "ĠLiberals": 41172, + "æ©Ł": 41173, + "Ġaudi": 41174, + "ĠPolar": 41175, + "Ġrhythms": 41176, + "ĠNI": 41177, + "olphin": 41178, + "SPI": 41179, + "Ġsuperoxide": 41180, + "gens": 41181, + "Ġvillagers": 41182, + "ĠJacobs": 41183, + "Ġgeometries": 41184, + "Ġbiting": 41185, + "ĠEva": 41186, + "STRA": 41187, + "ENO": 41188, + "lı": 41189, + "Ġdismay": 41190, + "Kam": 41191, + "Were": 41192, + "increasing": 41193, + ",{": 41194, + "Ġentertained": 41195, + "ĠFalcon": 41196, + "æĥ³": 41197, + "ĠJamaica": 41198, + "Ġdoit": 41199, + "gtk": 41200, + "Ġchampagne": 41201, + "模": 41202, + "Ġseeming": 41203, + "Ġtowels": 41204, + "Validator": 41205, + "Ġ381": 41206, + "Ġpeasant": 41207, + "Nu": 41208, + "Ġblending": 41209, + "::::": 41210, + "575": 41211, + "Ġimprint": 41212, + "ovanni": 41213, + "inia": 41214, + "Ġpendant": 41215, + "ĠAbe": 41216, + "Ġdiscovers": 41217, + "ĠRIP": 41218, + "Ġ447": 41219, + "ĠAnalytics": 41220, + "ĠEdited": 41221, + "Ġanalogues": 41222, + "Ġtribunal": 41223, + "ĠLogic": 41224, + "Medical": 41225, + "raf": 41226, + "551": 41227, + "ĠQuart": 41228, + "cardi": 41229, + "åĵģ": 41230, + "avo": 41231, + "OLOG": 41232, + "rounds": 41233, + "Ġgenotyping": 41234, + "ĠPerkins": 41235, + "Ġwhiskey": 41236, + "Jason": 41237, + "ĠPROF": 41238, + "YA": 41239, + "Ġsandy": 41240, + "ativo": 41241, + "rico": 41242, + "Ġpadd": 41243, + "Ġintracranial": 41244, + "uden": 41245, + "yyyy": 41246, + "Ġ^*": 41247, + "Ġcontiguous": 41248, + "Ġechoes": 41249, + "å¿ħ": 41250, + "ç»Ħ": 41251, + "ISM": 41252, + "Ġdieser": 41253, + "Ġë": 41254, + "Others": 41255, + ")],": 41256, + "icorn": 41257, + "ĠBaseline": 41258, + "othelium": 41259, + "ĠAde": 41260, + "Ġrugged": 41261, + "Pull": 41262, + "Negative": 41263, + "ĠHers": 41264, + "ollywood": 41265, + "blr": 41266, + "Ġ394": 41267, + "ĠUSC": 41268, + "Continue": 41269, + "UIT": 41270, + "Ġreceipts": 41271, + "atosis": 41272, + "ĠElements": 41273, + "Ġbulky": 41274, + "ĠIntegrated": 41275, + "Ġozone": 41276, + "ĠJuda": 41277, + "ĠDro": 41278, + "ĠAX": 41279, + "094": 41280, + "Ġ398": 41281, + "filters": 41282, + "ĠTradition": 41283, + "/((-": 41284, + "wey": 41285, + "nec": 41286, + "apid": 41287, + "Ġpane": 41288, + "Ġfortress": 41289, + "Ġameric": 41290, + "Ġkw": 41291, + "Ġconnective": 41292, + "carb": 41293, + "Ġbranded": 41294, + "inetics": 41295, + "Song": 41296, + "Ġdominating": 41297, + "chro": 41298, + "Ġinertia": 41299, + "Ġhydrochlor": 41300, + "ĠâĢĭ": 41301, + "},$": 41302, + "corner": 41303, + "ĠÃ¥r": 41304, + "]{}\\_[": 41305, + "'.\"": 41306, + "Ġbuys": 41307, + "counsel": 41308, + 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45186, + "ĠDeutsche": 45187, + "Ġhär": 45188, + "mozilla": 45189, + "Ġempirically": 45190, + "HANDLE": 45191, + "Ġintoxicated": 45192, + "oform": 45193, + "inai": 45194, + "overrightarrow": 45195, + "Ġarbitrator": 45196, + "087": 45197, + "ción": 45198, + "Ġfreshwater": 45199, + "Ġpioneering": 45200, + ")\\*": 45201, + "1942": 45202, + "Ġorganizer": 45203, + "963": 45204, + "amis": 45205, + "),$": 45206, + "ĠNigerian": 45207, + "Ġplasmon": 45208, + "Gordon": 45209, + "Ġborderline": 45210, + "ĠImplementation": 45211, + "Username": 45212, + "ĠIgA": 45213, + "ĠManufacturing": 45214, + "Ġmuss": 45215, + "ım": 45216, + "heed": 45217, + "enzyme": 45218, + "Ġtrusts": 45219, + "Overflow": 45220, + "ĠPreparation": 45221, + "ideal": 45222, + "Ġ492": 45223, + "Ġimmensely": 45224, + "534": 45225, + "ĠWeaver": 45226, + "ĠClasses": 45227, + "Ġorbitals": 45228, + "Ġaccrued": 45229, + "therefore": 45230, + "FFF": 45231, + "ĠØ®": 45232, + "hookrightarrow": 45233, + "Ġpessim": 45234, + "Ġconsultations": 45235, + "ĠHAD": 45236, + "ĠshRNA": 45237, + "Ġpalate": 45238, + "ĠCTR": 45239, + "5555": 45240, + "assembled": 45241, + "largest": 45242, + "igrant": 45243, + "Ġhumiliation": 45244, + "Prince": 45245, + "ĠNielsen": 45246, + "Ġâĸł": 45247, + "ĠManit": 45248, + "空": 45249, + "Ġ{:": 45250, + "}})$,": 45251, + "inkling": 45252, + "Ġintensified": 45253, + "Ġcautiously": 45254, + "Charlie": 45255, + "åĢĭ": 45256, + "ĠBj": 45257, + "ĠScene": 45258, + "544": 45259, + "worked": 45260, + "çķĮ": 45261, + "ĠBurk": 45262, + "--------------------------------------------------": 45263, + "Ġarousal": 45264, + "Ġ1897": 45265, + "Ġchoke": 45266, + "Ġgeo": 45267, + "bund": 45268, + "Ġectopic": 45269, + "pv": 45270, + "Ġlongitude": 45271, + "Ġ1889": 45272, + "ĠScientists": 45273, + "ĠCarrie": 45274, + "ĠZnO": 45275, + "pts": 45276, + "ĠTrudeau": 45277, + "Ġmorphisms": 45278, + "Ġforwarding": 45279, + "ĠHilton": 45280, + "\":\"\",": 45281, + "ĠRé": 45282, + "grave": 45283, + "rored": 45284, + "Ġdiam": 45285, + "Ġimpregn": 45286, + "ĠTeaching": 45287, + "Ġstraps": 45288, + "arf": 45289, + "Ġholistic": 45290, + "ĠOpposition": 45291, + "ĠCisco": 45292, + "MATCH": 45293, + "Ġbrute": 45294, + "8859": 45295, + "bolt": 45296, + "Ġ548": 45297, + "Ġ666": 45298, + "øre": 45299, + "Ġneonates": 45300, + "]\\\\": 45301, + "phalt": 45302, + "nop": 45303, + "Ġhosp": 45304, + "Ġconoc": 45305, + "FIF": 45306, + "ĠHö": 45307, + "Ġbelongings": 45308, + "ĠRicci": 45309, + "Ġtir": 45310, + "Ġfinanced": 45311, + "CDATA": 45312, + "cloth": 45313, + "æĶ¯": 45314, + "âĶĢâĶĢâĶĢâĶĢâĶĢâĶĢâĶĢâĶĢ": 45315, + "529": 45316, + "Ruby": 45317, + "Dun": 45318, + "Ġcalculates": 45319, + "Ġbipartisan": 45320, + "alam": 45321, + "ĠMSS": 45322, + "ĠDodge": 45323, + "ringes": 45324, + "ĠMaker": 45325, + "ĠAnnex": 45326, + "Flash": 45327, + "dad": 45328, + "mV": 45329, + "ETHERTYPE": 45330, + "nad": 45331, + "**~": 45332, + "Ġ701": 45333, + "Ġscor": 45334, + "TU": 45335, + "eson": 45336, + "cket": 45337, + "Ġaxonal": 45338, 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45494, + "Ġ_________________________________": 45495, + "Ġ(!(": 45496, + "Ġmasterpiece": 45497, + "Ġdext": 45498, + "tokens": 45499, + "ĠActing": 45500, + "imple": 45501, + "Ġ519": 45502, + "ĠBella": 45503, + "Ġcommissioners": 45504, + "Ġmb": 45505, + "ĠFurn": 45506, + "Ġ](": 45507, + "å°ij": 45508, + "\"}^": 45509, + "atation": 45510, + "Ġfueled": 45511, + "Ġpiercing": 45512, + "Ġgovernors": 45513, + "ESH": 45514, + "Ġunbounded": 45515, + "ĠPLAY": 45516, + "Ġoccupations": 45517, + "dirname": 45518, + "sponsored": 45519, + "Ġnaj": 45520, + "Ġgrandes": 45521, + "Ġalarmed": 45522, + "ĠBLAST": 45523, + "gni": 45524, + "planation": 45525, + "ĠBulls": 45526, + "Ġimporting": 45527, + "!âĢĻ": 45528, + "aside": 45529, + "ĠOsw": 45530, + "Ġsparkling": 45531, + "Ik": 45532, + "546": 45533, + "ĠCHO": 45534, + "1006": 45535, + "ĠEuroopan": 45536, + "eti": 45537, + "LH": 45538, + "asic": 45539, + "Ġstature": 45540, + "enthal": 45541, + "conc": 45542, + "Ġhumili": 45543, + "Ġquota": 45544, + 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"integrated": 45595, + "ĠBold": 45596, + "ĠÑĢабоÑĤ": 45597, + "ĠEmpty": 45598, + ";|": 45599, + "ĠPressure": 45600, + "ĠDistance": 45601, + "ĠMRSA": 45602, + "ĠPacket": 45603, + "Ġlado": 45604, + "ĠCXCR": 45605, + "Ring": 45606, + "Market": 45607, + "Ġsabot": 45608, + "zitter": 45609, + "Ġ539": 45610, + "Ġinconsistencies": 45611, + "Ġechocardiography": 45612, + "Ġbioactive": 45613, + "ĠSiO": 45614, + "Road": 45615, + "Ġsuperposition": 45616, + "ĠFOUR": 45617, + "distributed": 45618, + "OCT": 45619, + "Ġtenderness": 45620, + "Tan": 45621, + "ĠEEOC": 45622, + "hew": 45623, + "elves": 45624, + "Ġlepton": 45625, + "ĠExped": 45626, + "chev": 45627, + "Ġoccupant": 45628, + ".|": 45629, + "Ġrb": 45630, + "Ġév": 45631, + "Ġantisense": 45632, + "Ġbri": 45633, + "completion": 45634, + "('<": 45635, + "Ġparaly": 45636, + "Ġ(\\#": 45637, + "ĠFAM": 45638, + "Ġdizz": 45639, + "Ġkeyboards": 45640, + "Ġtoim": 45641, + "andals": 45642, + "Ġcharities": 45643, + "Ġdiseased": 45644, + "Ġwarp": 45645, + "Ġpolitely": 45646, + "opedic": 45647, + "Ġnmol": 45648, + "Refresh": 45649, + "Ġobscured": 45650, + "Ġbillionaire": 45651, + "Ġlia": 45652, + "Completed": 45653, + "Ġintrinsically": 45654, + "ĠTina": 45655, + "Ġ{{{\\": 45656, + "underbrace": 45657, + "Ġsheaf": 45658, + "ĠSpacewatch": 45659, + "Ġmillennium": 45660, + "Ġpinpoint": 45661, + "\\]](": 45662, + "ĠBorough": 45663, + "ĠAllison": 45664, + "Ġprotesting": 45665, + "Ġsimplicial": 45666, + "Ġcouncils": 45667, + "Ric": 45668, + "uxe": 45669, + "trees": 45670, + "Ġcraving": 45671, + "Ġomin": 45672, + "ĠDud": 45673, + "ĠOrient": 45674, + "Ġsqlite": 45675, + "Ġinscription": 45676, + "\\\\\\": 45677, + "Ġlodged": 45678, + "clo": 45679, + "Ġihm": 45680, + "ĠLudwig": 45681, + "Developer": 45682, + "æł¼": 45683, + "PSCs": 45684, + "Äģr": 45685, + "boats": 45686, + "Ġdripping": 45687, + "Ġcriticize": 45688, + "dong": 45689, + "ĠScots": 45690, + "ĠCunningham": 45691, + "Ġheterosexual": 45692, + "ظ": 45693, + "agreement": 45694, + "oppy": 45695, + "Ġdiscreet": 45696, + "Ġanecd": 45697, + "705": 45698, + "ĠPair": 45699, + "Ġvibe": 45700, + "0039": 45701, + "atinib": 45702, + "Ġpertains": 45703, + "ĠBEL": 45704, + "Ġphenol": 45705, + "ĠAnalyses": 45706, + "}}})$": 45707, + "ĠLogger": 45708, + "ĠAgents": 45709, + "ĠExtended": 45710, + "ĠKathleen": 45711, + "itars": 45712, + "ĠSBP": 45713, + "ĠHogan": 45714, + "ieurs": 45715, + "velle": 45716, + "UIC": 45717, + "Ġwsp": 45718, + ",'\"": 45719, + "ĠObservable": 45720, + "Nat": 45721, + "ĠMiz": 45722, + "KERNEL": 45723, + "ĠPole": 45724, + "okrat": 45725, + "Ġpurge": 45726, + "Ġply": 45727, + "ĠDHS": 45728, + "ĠHammond": 45729, + "Ġ768": 45730, + "Ġbaths": 45731, + "Ġfide": 45732, + "ĠBattery": 45733, + "ĠMeaning": 45734, + "Ġrevisit": 45735, + "ĠCTC": 45736, + "Ġunpack": 45737, + "Ġimitation": 45738, + "thems": 45739, + "ĠSTE": 45740, + "inco": 45741, + "Ġlowers": 45742, + "Above": 45743, + "ĠOdys": 45744, + "icum": 45745, + "ĠHodge": 45746, + "Ġspraying": 45747, + "身": 45748, + "arbit": 45749, + "1937": 45750, + "ĠPhotograph": 45751, + "Ġcranial": 45752, + "Ġcoma": 45753, + "omechanical": 45754, + "Ġredesign": 45755, + "irical": 45756, + "unnable": 45757, + "Ġsailor": 45758, + "611": 45759, + "procedure": 45760, + "Virgin": 45761, + "ĠWriters": 45762, + "ĠHert": 45763, + "Ġarisen": 45764, + "Ġdecomposed": 45765, + "Ġdagger": 45766, + "matching": 45767, + "Ġ%{": 45768, + "Ġsemen": 45769, + "ĠGoodman": 45770, + "Ġsubtracting": 45771, + "Enjoy": 45772, + "ĠEliot": 45773, + "636": 45774, + "Ġdecoded": 45775, + "ãģ§ãĤĤ": 45776, + "Ġtibial": 45777, + "ĠBrom": 45778, + "mium": 45779, + "Ġbets": 45780, + "Ġinvocation": 45781, + "ĠBread": 45782, + "Ġimpractical": 45783, + "Ġroofs": 45784, + "Ġrubbish": 45785, + "bels": 45786, + "Nan": 45787, + "Ġlush": 45788, + "Ġtextbooks": 45789, + "ĠOlivia": 45790, + "hf": 45791, + "RESULTS": 45792, + "Ġcleft": 45793, + "boundary": 45794, + "gia": 45795, + "Ġdib": 45796, + "ĠDew": 45797, + "Ġrestricts": 45798, + "ImageView": 45799, + "Ġascribed": 45800, + "ák": 45801, + "ĠLok": 45802, + "Ġchancellor": 45803, + "ç§ģ": 45804, + "Ġcytoskeleton": 45805, + "ignant": 45806, + "Ġhust": 45807, + "ecal": 45808, + "Ðij": 45809, + "Ġerotic": 45810, + "Ġnarrator": 45811, + "ĠCic": 45812, + "odend": 45813, + "Ġoddly": 45814, + "lund": 45815, + "ĠAmin": 45816, + "ĠOriental": 45817, + "(#": 45818, + "1936": 45819, + "Ġmotto": 45820, + "Ù¾": 45821, + "Ġgraceful": 45822, + "swers": 45823, + "aggio": 45824, + "alar": 45825, + "thead": 45826, + "910": 45827, + "ĠNil": 45828, + "Ġscall": 45829, + "Ġct": 45830, + "zol": 45831, + "ĠKod": 45832, + "Ġunsaturated": 45833, + "Ġ1862": 45834, + "Ġ-------------------------------": 45835, + "avez": 45836, + "mmHg": 45837, + "ĠXin": 45838, + "OLOR": 45839, + "Patrick": 45840, + "Ġseaw": 45841, + "contrast": 45842, + "Ġ$@": 45843, + "Ġvara": 45844, + "ĠTribunal": 45845, + "hta": 45846, + "...?\"": 45847, + "Ġhandheld": 45848, + "Ġwellness": 45849, + "Ġhw": 45850, + "ĠMage": 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