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README.md
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# Llama3.1-8B-Chinese-Chat_a13660991510540288444958
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---
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license: llama3.1
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library_name: transformers
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pipeline_tag: text-generation
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base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
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language:
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- en
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- zh
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tags:
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- llama-factory
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- orpo
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---
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> [!CAUTION]
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> For optimal performance, we refrain from fine-tuning the model's identity. Thus, inquiries such as "Who are you" or "Who developed you" may yield random responses that are not necessarily accurate.
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> [!IMPORTANT]
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> If you enjoy our model, please **give it a star on our Hugging Face repo** and kindly [**cite our model**](https://huggingface.co/shenzhi-wang/Llama3.1-8B-Chinese-Chat#citation). Your support means a lot to us. Thank you!
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# Updates
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- 🚀🚀🚀 [July 24, 2024] We now introduce [shenzhi-wang/Llama3.1-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3.1-8B-Chinese-Chat)! The training dataset contains >100K preference pairs, and it exhibits significant enhancements, especially in **roleplay**, **function calling**, and **math** capabilities!
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- 🔥 We provide the official **q4_k_m, q8_0, and f16 GGUF** versions of Llama3.1-8B-Chinese-Chat-**v2.1** at https://huggingface.co/shenzhi-wang/Llama3.1-8B-Chinese-Chat/tree/main/gguf!
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# Model Summary
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llama3.1-8B-Chinese-Chat is an instruction-tuned language model for Chinese & English users with various abilities such as roleplaying & tool-using built upon the Meta-Llama-3.1-8B-Instruct model.
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Developers: [Shenzhi Wang](https://shenzhi-wang.netlify.app)\*, [Yaowei Zheng](https://github.com/hiyouga)\*, Guoyin Wang (in.ai), Shiji Song, Gao Huang. (\*: Equal Contribution)
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- License: [Llama-3.1 License](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B/blob/main/LICENSE)
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- Base Model: Meta-Llama-3.1-8B-Instruct
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- Model Size: 8.03B
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- Context length: 128K (reported by [Meta-Llama-3.1-8B-Instruct model](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct), untested for our Chinese model)
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# 1. Introduction
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This is the first model specifically fine-tuned for Chinese & English users based on the [Meta-Llama-3.1-8B-Instruct model](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct). The fine-tuning algorithm used is ORPO [1].
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[1] Hong, Jiwoo, Noah Lee, and James Thorne. "Reference-free Monolithic Preference Optimization with Odds Ratio." arXiv preprint arXiv:2403.07691 (2024).
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Training framework: [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory).
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Training details:
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- epochs: 3
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- learning rate: 3e-6
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- learning rate scheduler type: cosine
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- Warmup ratio: 0.1
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- cutoff len (i.e. context length): 8192
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- orpo beta (i.e. $\lambda$ in the ORPO paper): 0.05
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- global batch size: 128
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- fine-tuning type: full parameters
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- optimizer: paged_adamw_32bit
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# 2. Usage
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## 2.1 Usage of Our BF16 Model
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1. Please upgrade the `transformers` package to ensure it supports Llama3.1 models. The current version we are using is `4.43.0`.
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2. Use the following Python script to download our BF16 model
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```python
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from huggingface_hub import snapshot_download
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snapshot_download(repo_id="shenzhi-wang/Llama3.1-8B-Chinese-Chat", ignore_patterns=["*.gguf"]) # Download our BF16 model without downloading GGUF models.
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```
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3. Inference with the BF16 model
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```python
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import torch
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import transformers
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "/Your/Local/Path/to/Llama3.1-8B-Chinese-Chat"
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dtype = torch.bfloat16
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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device_map="cuda",
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torch_dtype=dtype,
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)
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chat = [
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{"role": "user", "content": "写一首关于机器学习的诗。"},
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]
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input_ids = tokenizer.apply_chat_template(
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chat, tokenize=True, add_generation_prompt=True, return_tensors="pt"
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).to(model.device)
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outputs = model.generate(
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input_ids,
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max_new_tokens=8192,
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do_sample=True,
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temperature=0.6,
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top_p=0.9,
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)
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response = outputs[0][input_ids.shape[-1] :]
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print(tokenizer.decode(response, skip_special_tokens=True))
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```
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## 2.2 Usage of Our GGUF Models
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1. Download our GGUF models from the [gguf_models folder](https://huggingface.co/shenzhi-wang/Llama3.1-8B-Chinese-Chat/tree/main/gguf);
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2. Use the GGUF models with [LM Studio](https://lmstudio.ai/);
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3. You can also follow the instructions from https://github.com/ggerganov/llama.cpp/tree/master#usage to use gguf models.
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# Citation
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If our Llama3.1-8B-Chinese-Chat is helpful, please kindly cite as:
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```
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@misc {shenzhi_wang_2024,
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author = { Wang, Shenzhi and Zheng, Yaowei and Wang, Guoyin and Song, Shiji and Huang, Gao },
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title = { Llama3.1-8B-Chinese-Chat },
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year = 2024,
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url = { https://huggingface.co/shenzhi-wang/Llama3.1-8B-Chinese-Chat },
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doi = { 10.57967/hf/2779 },
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publisher = { Hugging Face }
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}
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```
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Llama3.1-8B-Chinese-Chat
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{
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"_name_or_path": "shenzhi-wang/Llama3.1-8B-Chinese-Chat",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": [
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128001,
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128008,
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128009
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],
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 8.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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},
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.44.0.dev0",
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"use_cache": true,
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"vocab_size": 128256
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}
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{"framework": "pytorch", "task": "content generation", "allow_remote": true}
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{
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@ -0,0 +1,17 @@
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Reference in New Issue