nvidia / AceInstruct-1.5B

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 . These same datasets are also used in the training of AceMath-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 , AceMath-7B-Instruct , AceMath-72B-Instruct

AceMath Reward Models

• AceMath-7B-RM , AceMath-72B-RM

Evaluation & Training Data

• AceMath-RewardBench , AceMath-Instruct Training Data , AceMath-RM Training Data

General Instruction Models

• AceInstruct-1.5B , AceInstruct-7B , AceInstruct-72B

How to use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "AceInstruct-1.5B"
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.

@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 of the data generated by OpenAI. We put the AceInstruct models under the license of Creative Commons Attribution: Non-Commercial 4.0 International .