README.md

---
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-7B"
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 ([email protected]), Yang Chen ([email protected]), Wei Ping ([email protected])


## 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).