Create README.md

README.md

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+---
+license: cc-by-nc-4.0
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- nvidia
+- AceInstruct
+- code
+- math
+- general_domain
+- instruct_model
+- pytorch
+---
+
+## Introduction
+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>.
+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.
+
+For more information about AceInstruct, check our [website](https://research.nvidia.com/labs/adlr/acemath/) and [paper](https://arxiv.org/abs/2412.15084).
+
+
+## Benchmark Results
+| | Qwen2.5-1.5B-Instruct | AceInstruct-1.5B | Qwen2.5-7B-Instruct | AceInstruct-7B | Qwen2.5-72B-Instruct | AceInstruct-72B | 
+| --------- |:-----:|:-----:|:-----:|:-----:|:-----:|:-----:|
+| HumanEval | 61.60 | 73.17 | 84.80 | 85.37 | 86.60 | 89.63 | 
+| MBPP      | 63.20 | 65.76 | 79.20 | 74.32 | 88.20 | 83.66 |
+| GSM8K     | 73.20 | 80.44 | 91.60 | 93.10 | 95.80 | 96.36 |
+| MATH      | 55.20 | 60.34 | 75.50 | 76.40 | 83.10 | 84.50 |
+| MMLU      | 58.37 | 58.17 | 74.51 | 74.68 | 84.67 | 83.88 |
+| MMLU Pro  | 32.40 | 33.78 | 56.30 | 54.50 | 71.10 | 66.10 |
+| Average   | 57.33 | 61.94 | 76.99 | 76.40 | 84.91 | 84.02 |
+
+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.
+
+
+## All Resources
+### AceMath Instruction Models
+- [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)
+
+### AceMath Reward Models
+- [AceMath-7B-RM](https://huggingface.co/nvidia/AceMath-7B-RM), [AceMath-72B-RM](https://huggingface.co/nvidia/AceMath-72B-RM)
+
+### Evaluation & Training Data
+- [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)
+
+### General Instruction Models
+- [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)
+
+
+## How to use
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "AceInstruct-72B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
+
+prompt = "Tell me something about artificial intelligence."
+messages = [{"role": "user", "content": prompt}]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to("cuda")
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=1024
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+
+
+## Correspondence to
+Zihan Liu (zihanl@nvidia.com), Yang Chen (yachen@nvidia.com), Wei Ping (wping@nvidia.com)
+
+
+## Citation
+If you find our work helpful, we’d appreciate it if you could cite us.
+<pre>
+@article{acemath2024,
+  title={AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling},
+  author={Liu, Zihan and Chen, Yang and Shoeybi, Mohammad and Catanzaro, Bryan and Ping, Wei},
+  journal={arXiv preprint},
+  year={2024}
+}
+</pre>
+
+
+## License
+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).