Aceinstruct 7B

🚀 AceInstruct: Advanced SFT Models

AceInstruct is a family of advanced SFT models designed for coding, mathematics, and general - purpose tasks. It offers high - performance solutions across multiple domains, with performance comparable to Qwen2.5 - Instruct.

🚀 Quick Start

AceInstruct can be easily used with the following Python code. It demonstrates how to load the model, tokenize input, and generate responses.

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]

✨ Features

• Versatile Application: Different from the math - specialized AceMath - Instruct, AceInstruct can be applied to a wide range of domains, including coding, mathematics, and general knowledge tasks.
• High Performance: Benchmark evaluations show that AceInstruct delivers performance comparable to Qwen2.5 - Instruct. In particular, AceInstruct - 1.5B outperforms Qwen2.5 - 1.5B - Instruct.

📚 Documentation

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 Improved using Qwen. 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 and paper.

Benchmark Results

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)

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.

@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}
}

📄 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).