--- title: AceInstruct-72B canonical_url: "https://www.modelscope.cn/models/nv-community/AceInstruct-72B" md_url: "https://www.modelscope.cn/models/nv-community/AceInstruct-72B.md" repository: nv-community/AceInstruct-72B last_updated: 2025-04-17 license: cc-by-nc-4.0 pipeline_tag: text-generation tasks: - text-generation model_type: - qwen2 architectures: - Qwen2ForCausalLM parameters: 72.7B tensor_type: - BF16 library_name: - transformer - safetensors - pytorch frameworks: - pytorch language: - en inference_backends: - "deploy_task text/emb" - "lmdeploy 0.9.1" - "lmdeploy_turbomind 0.9.1" - "sglang 0.5.2" - "vllm 0.9.2" downloads: 759 stars: 0 tags: - nvidia - AceInstruct - code - math - general_domain - instruct_model - pytorch --- # AceInstruct-72B > AceInstruct-72B - nv-community 在 ModelScope 开源的模型。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… nv-community/AceInstruct-72B 是 ModelScope 魔搭社区上的 72.7B 参数text-generation模型，采用 cc-by-nc-4.0 许可，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。 - **Repository**: nv-community/AceInstruct-72B - **License**: cc-by-nc-4.0 - **Tasks**: text-generation - **Parameters**: 72.7B - **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2 - **Tags**: nvidia, AceInstruct, code, math, general_domain, instruct_model, pytorch - **Downloads**: 759 - **Stars**: 0 - **Last updated**: 2025-04-17 Source: https://www.modelscope.cn/models/nv-community/AceInstruct-72B --- ## 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](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. 
@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).