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
| 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 Reward Models
Evaluation & Training Data
General Instruction Models
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 (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.
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.