98 lines
4.6 KiB
Markdown
98 lines
4.6 KiB
Markdown
---
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license: cc-by-nc-4.0
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language:
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- en
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pipeline_tag: text-generation
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tags:
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- nvidia
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- AceInstruct
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- code
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- math
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- general_domain
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- instruct_model
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- pytorch
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---
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## Introduction
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We introduce AceInstruct, a family of advanced SFT models for coding, mathematics, and general-purpose tasks. The AceInstruct family, which includes AceInstruct-1.5B, 7B, and 72B, is <b>Improved using Qwen</b>.
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These models are fine-tuned on Qwen2.5-Base using [general SFT datasets](https://huggingface.co/datasets/nvidia/AceMath-Instruct-Training-Data). These same datasets are also used in the training of [AceMath-Instruct](https://huggingface.co/nvidia/AceMath-72B-Instruct). Different from AceMath-Instruct which is specialized for math questions, AceInstruct is versatile and can be applied to a wide range of domains. Benchmark evaluations across coding, mathematics, and general knowledge tasks demonstrate that AceInstruct delivers performance comparable to Qwen2.5-Instruct.
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For more information about AceInstruct, check our [website](https://research.nvidia.com/labs/adlr/acemath/) and [paper](https://arxiv.org/abs/2412.15084).
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## Benchmark Results
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| | Qwen2.5-1.5B-Instruct | AceInstruct-1.5B | Qwen2.5-7B-Instruct | AceInstruct-7B | Qwen2.5-72B-Instruct | AceInstruct-72B |
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| --------- |:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
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| HumanEval | 61.60 | 73.17 | 84.80 | 85.37 | 86.60 | 89.63 |
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| MBPP | 63.20 | 65.76 | 79.20 | 74.32 | 88.20 | 83.66 |
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| GSM8K | 73.20 | 80.44 | 91.60 | 93.10 | 95.80 | 96.36 |
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| MATH | 55.20 | 60.34 | 75.50 | 76.40 | 83.10 | 84.50 |
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| MMLU | 58.37 | 58.17 | 74.51 | 74.68 | 84.67 | 83.88 |
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| MMLU Pro | 32.40 | 33.78 | 56.30 | 54.50 | 71.10 | 66.10 |
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| Average | 57.33 | 61.94 | 76.99 | 76.40 | 84.91 | 84.02 |
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We compare AceInstruct to Qwen2.5-Instruct across coding, mathematics, and general knowledge tasks. We find that AceInstruct-1.5B outperforms Qwen2.5-1.5B-Instruct (61.94 vs. 57.33), while AceInstruct-7B and AceInstruct-72B perform similarly to Qwen2.5-7B-Instruct and Qwen2.5-72B-Instruct.
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## All Resources
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### AceMath Instruction Models
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- [AceMath-1.5B-Instruct](https://huggingface.co/nvidia/AceMath-1.5B-Instruct), [AceMath-7B-Instruct](https://huggingface.co/nvidia/AceMath-7B-Instruct), [AceMath-72B-Instruct](https://huggingface.co/nvidia/AceMath-72B-Instruct)
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### AceMath Reward Models
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- [AceMath-7B-RM](https://huggingface.co/nvidia/AceMath-7B-RM), [AceMath-72B-RM](https://huggingface.co/nvidia/AceMath-72B-RM)
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### Evaluation & Training Data
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- [AceMath-RewardBench](https://huggingface.co/datasets/nvidia/AceMath-RewardBench), [AceMath-Instruct Training Data](https://huggingface.co/datasets/nvidia/AceMath-Instruct-Training-Data), [AceMath-RM Training Data](https://huggingface.co/datasets/nvidia/AceMath-RM-Training-Data)
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### General Instruction Models
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- [AceInstruct-1.5B](https://huggingface.co/nvidia/AceInstruct-1.5B), [AceInstruct-7B](https://huggingface.co/nvidia/AceInstruct-7B), [AceInstruct-72B](https://huggingface.co/nvidia/AceInstruct-72B)
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## How to use
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "AceInstruct-1.5B"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
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prompt = "Tell me something about artificial intelligence."
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messages = [{"role": "user", "content": prompt}]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to("cuda")
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=1024
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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## Correspondence to
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Zihan Liu (zihanl@nvidia.com), Yang Chen (yachen@nvidia.com), Wei Ping (wping@nvidia.com)
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## Citation
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If you find our work helpful, we’d appreciate it if you could cite us.
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<pre>
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@article{acemath2024,
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title={AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling},
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author={Liu, Zihan and Chen, Yang and Shoeybi, Mohammad and Catanzaro, Bryan and Ping, Wei},
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journal={arXiv preprint},
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year={2024}
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}
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</pre>
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## License
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All models in the AceInstruct family are for non-commercial use only, subject to [Terms of Use](https://openai.com/policies/row-terms-of-use/) of the data generated by OpenAI. We put the AceInstruct models under the license of [Creative Commons Attribution: Non-Commercial 4.0 International](https://spdx.org/licenses/CC-BY-NC-4.0). |