commit 21c9ab1f0d887d1757964674b882f76c81ba31a8 Author: ModelHub XC Date: Sat Jun 6 08:18:43 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: nvidia/AceReason-Nemotron-14B Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..f558c57 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,37 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +*.png filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..9f9d1ed --- /dev/null +++ b/README.md @@ -0,0 +1,146 @@ + + + +--- +library_name: transformers +license: other +license_name: nvidia-open-model-license +license_link: >- + https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/ +pipeline_tag: text-generation +language: + - en +tags: + - nvidia + - reasoning + - math + - code + - reinforcement learning + - pytorch +--- + +# AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning + +

+ +[![Technical Report](https://img.shields.io/badge/2505.16400-Technical_Report-blue)](https://arxiv.org/abs/2505.16400) +[![Dataset](https://img.shields.io/badge/🤗-Math_RL_Datset-blue)](https://huggingface.co/datasets/nvidia/AceReason-Math) +[![Models](https://img.shields.io/badge/🤗-Models-blue)](https://huggingface.co/collections/nvidia/acereason-682f4e1261dc22f697fd1485) +[![Eval Toolkit](https://img.shields.io/badge/🤗-Eval_Code-blue)](https://huggingface.co/nvidia/AceReason-Nemotron-14B/blob/main/README_EVALUATION.md) +

+ + main_fig + +## 🔥News +- **6/16/2025**: We are excited to share our new release combining SFT with RL: **AceReason-Nemotron-1.1-7B** + - Paper: https://arxiv.org/pdf/2506.13284 + - Model: https://huggingface.co/nvidia/AceReason-Nemotron-1.1-7B + - 4M SFT Data: https://huggingface.co/datasets/nvidia/AceReason-1.1-SFT +- **6/11/2025**: We share our evaluation toolkit at [AceReason Evalution](https://huggingface.co/nvidia/AceReason-Nemotron-14B/blob/main/README_EVALUATION.md) including: + - scripts to run inference and scoring + - LiveCodeBench (avg@8): model prediction files and scores for each month (2023/5-2025/5) + - AIME24/25 (avg@64): model prediction files and scores +- **6/2/2025**: We are excited to share our Math RL training dataset at [AceReason-Math](https://huggingface.co/datasets/nvidia/AceReason-Math) + +We're thrilled to introduce AceReason-Nemotron-14B, a math and code reasoning model trained entirely through reinforcement learning (RL), starting from the DeepSeek-R1-Distilled-Qwen-14B. It delivers impressive results, achieving 78.6% on AIME 2024 (+8.9%), 67.4% on AIME 2025 (+17.4%), 61.1% on LiveCodeBench v5 (+8%), 54.9% on LiveCodeBench v6 (+7%), and 2024 on Codeforces (+543). We systematically study the RL training process through extensive ablations and propose a simple yet effective approach: first RL training on math-only prompts, then RL training on code-only prompts. Notably, we find that math-only RL not only significantly enhances the performance of strong distilled models on math benchmarks, but also code reasoning tasks. In addition, extended code-only RL further improves code benchmark performance while causing minimal degradation in math results. We find that RL not only elicits the foundational reasoning capabilities acquired during pre-training and supervised fine-tuning (e.g., distillation), but also pushes the limits of the model's reasoning ability, enabling it to solve problems that were previously unsolvable. + +We share our training recipe, training logs in our [technical report](https://arxiv.org/abs/2505.16400). + +## Results + +We evaluate our model against competitive reasoning models of comparable size within Qwen2.5 and Llama3.1 model family on AIME 2024, AIME 2025, LiveCodeBench v5 (2024/08/01 - 2025/02/01), and LiveCodeBench v6 (2025/02/01-2025/05/01). More evaluation results can be found in our [technical report](https://arxiv.org/abs/2505.16400). + +| **Model** | **AIME 2024
(avg@64)** | **AIME 2025
(avg@64)** | **LCB v5
(avg@8)** | **LCB v6
(avg@8)** | +| :---: | :---: | :---: | :---: | :---: | +| QwQ-32B | 79.5 | 65.8 | 63.4 | - | +| DeepSeek-R1-671B | 79.8 | 70.0 | 65.9 | - | +| Llama-Nemotron-Ultra-253B | 80.8 | 72.5 | 66.3 | - | +| o3-mini (medium) | 79.6 | 76.7 | 67.4 | - | +| Light-R1-14B | 74 | 60.2 | 57.9 | 51.5 | +| DeepCoder-14B (32K Inference) | 71 | 56.1 | 57.9 | 50.4 | +| OpenMath-Nemotron-14B | 76.3 | 63.0 | - | - | +| OpenCodeReasoning-Nemotron-14B | - | - | 59.4 | 54.1 | +| Llama-Nemotron-Super-49B-v1 | 67.5 | 60.0 | 45.5 | - | +| DeepSeek-R1-Distilled-Qwen-14B | 69.7 | 50.2 | 53.1 | 47.9 | +| DeepSeek-R1-Distilled-Qwen-32B | 72.6 | 54.9 | 57.2 | - | +| [AceReason-Nemotron-7B 🤗](https://huggingface.co/nvidia/AceReason-Nemotron-7B)| 69.0 | 53.6 | 51.8 | 44.1 | +| [AceReason-Nemotron-14B 🤗](https://huggingface.co/nvidia/AceReason-Nemotron-14B)| 78.6 | 67.4 | 61.1 | 54.9 | + + + + +## How to use +```python +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +model_name = 'nvidia/AceReason-Nemotron-14B' +tokenizer = AutoTokenizer.from_pretrained(model_name) +model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto") + +prompt = "Jen enters a lottery by picking $4$ distinct numbers from $S=\\{1,2,3,\\cdots,9,10\\}.$ $4$ numbers are randomly chosen from $S.$ She wins a prize if at least two of her numbers were $2$ of the randomly chosen numbers, and wins the grand prize if all four of her numbers were the randomly chosen numbers. The probability of her winning the grand prize given that she won a prize is $\\tfrac{m}{n}$ where $m$ and $n$ are relatively prime positive integers. Find $m+n$." +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=32768, + temperature=0.6, + top_p=0.95 +) +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] +``` + + +## Usage Recommendations + +1. Don't include a system prompt; instead, place all instructions directly in the user prompt. +2. We recommend using the following instruction for math questions: Please reason step by step, and put your final answer within \\boxed{}. +3. We recommend using the following instruction for code questions: +```python +question = "" # code question +starter_code = "" # starter code function header + +code_instruction_nostartercode = """Write Python code to solve the problem. Please place the solution code in the following format:\n```python\n# Your solution code here\n```""" +code_instruction_hasstartercode = """Please place the solution code in the following format:\n```python\n# Your solution code here\n```""" +if starter_code != "": + question += "\n\n" + "Solve the problem starting with the provided function header.\n\nFunction header:\n" + "```\n" + starter_code + "\n```" + question += "\n\n" + code_instruction_hasstartercode +else: + question += "\n\n" + code_instruction_nostartercode + +final_prompt = "<｜User｜>" + question + "<｜Assistant｜>\n" +``` +4. Our inference engine for evaluation is **vLLM==0.7.3** using top-p=0.95, temperature=0.6, max_tokens=32768. + +## Evaluation Toolkit + +Please check evaluation code, scripts, cached prediction files in https://huggingface.co/nvidia/AceReason-Nemotron-14B/blob/main/README_EVALUATION.md + +## Correspondence to +Yang Chen (yachen@nvidia.com), Zhuolin Yang (zhuoliny@nvidia.com), Zihan Liu (zihanl@nvidia.com), Chankyu Lee (chankyul@nvidia.com), Wei Ping (wping@nvidia.com) + + +## License +Your use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). + + +## Citation +``` +@article{chen2025acereason, + title={AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning}, + author={Chen, Yang and Yang, Zhuolin and Liu, Zihan and Lee, Chankyu and Xu, Peng and Shoeybi, Mohammad and Catanzaro, Bryan and Ping, Wei}, + journal={arXiv preprint arXiv:2505.16400}, + year={2025} +} +``` diff --git a/README_EVALUATION.md b/README_EVALUATION.md new file mode 100644 index 0000000..19e2dc2 --- /dev/null +++ b/README_EVALUATION.md @@ -0,0 +1,166 @@ +# AceReason Evaluation Toolkit + +We share our evaluation script and code in https://huggingface.co/nvidia/AceReason-Nemotron-14B/blob/main/evaluation.tar.gz + +## Environment +- vllm==0.7.3 +- torch==2.5.1 +- transformers==4.48.2 +- 8x NVIDIA H100 80GB HBM3 (CUDA Version: 12.8) + +### Dataset Download +LiveCodeBench: +``` +from datasets import load_dataset + +ds = load_dataset( + "livecodebench/code_generation_lite", + version_tag="release_v6", +)["test"] + +ds.to_json("data/livecodebench_problems.json", orient="records", lines=False) +``` + +Math: see data/* + +## Evaluation Script + +For model generation on single seed, please use the following command: + +``` +bash generate_livecodebench.sh ${model_path} ${seed} ${output_path} ${model_type} +bash generate_aime.sh ${model_path} ${seed} aime24 ${output_path} ${model_type} +bash generate_aime.sh ${model_path} ${seed} aime25 ${output_path} ${model_type} +``` +Please specify model_type as r1 for AceReason-Nemotron-1.0 models, and qwen for AceReason-Nemotron-1.1 models. + +Or you can use our configured seeds to reproduce our results on AIME 24/25 (avg@64) and LiveCodeBench v5/v6 (avg@8) as follows: + +``` +bash run_livecodebench.sh ${model_path} ${output_path} +bash run_aime.sh ${model_path} ${output_path} +``` + +For benchmark evaluation, we provide the following evaluation command to reproduce our results: + +``` +python evaluate_livecodebench.py -g ${output_path} +python evaluate_aime.py --modelfolder ${output_path} --test_data data/aime24.jsonl +python evaluate_aime.py --modelfolder ${output_path} --test_data data/aime25.jsonl +``` + +## Reference Results +We also left our generations into cache.tar.gz as references. + +``` +LiveCodeBench AceReason-Nemotron-1.0-7B (Avg@8) +================================================================= +Months Corrects Total Accuracy +2023-05 180 272 66.17647058823529 +2023-06 238 312 76.28205128205128 +2023-07 337 432 78.00925925925925 +2023-08 185 288 64.23611111111111 +2023-09 275 352 78.125 +2023-10 257 352 73.01136363636364 +2023-11 217 280 77.5 +2023-12 228 320 71.25 +2024-01 193 288 67.01388888888889 +2024-02 169 256 66.015625 +2024-03 234 360 65.0 +2024-04 226 296 76.35135135135135 +2024-05 211 288 73.26388888888889 +05/23-05/24 2950 4096 72.021484375 +2024-06 277 368 75.27173913043478 +2024-07 223 344 64.82558139534883 +2024-08 275 528 52.083333333333336 +2024-09 204 376 54.255319148936174 +2024-10 209 424 49.29245283018868 +2024-11 216 456 47.36842105263158 +2024-12 223 392 56.88775510204081 +2025-01 161 408 39.46078431372549 +06/24-01/25 1788 3296 54.24757281553398 +2025-02 179 408 43.872549019607845 +2025-03 258 544 47.4264705882353 +2025-04 38 96 39.583333333333336 +v5 1142 2232 51.16487455197132 +v6 621 1400 44.357142857142854 + +LiveCodeBench AceReason-Nemotron-1.0-14B (Avg@8) +================================================================= +Months Corrects Total Accuracy +2023-05 211 272 77.57352941176471 +2023-06 282 312 90.38461538461539 +2023-07 393 432 90.97222222222223 +2023-08 219 288 76.04166666666667 +2023-09 315 352 89.48863636363636 +2023-10 294 352 83.52272727272727 +2023-11 229 280 81.78571428571429 +2023-12 263 320 82.1875 +2024-01 219 288 76.04166666666667 +2024-02 201 256 78.515625 +2024-03 296 360 82.22222222222223 +2024-04 252 296 85.13513513513513 +2024-05 233 288 80.90277777777777 +05/23-05/24 3407 4096 83.1787109375 +2024-06 311 368 84.51086956521739 +2024-07 248 344 72.09302325581395 +2024-08 299 528 56.628787878787875 +2024-09 232 376 61.702127659574465 +2024-10 266 424 62.735849056603776 +2024-11 282 456 61.8421052631579 +2024-12 253 392 64.54081632653062 +2025-01 217 408 53.18627450980392 +06/24-01/25 2108 3296 63.95631067961165 +2025-02 211 408 51.71568627450981 +2025-03 324 544 59.55882352941177 +2025-04 41 96 42.708333333333336 +v5 1350 2232 60.483870967741936 +v6 775 1400 55.357142857142854 + +LiveCodeBench AceReason-Nemotron-1.1-7B (Avg@8) +================================================================= +Months Corrects Total Accuracy +2023-05 205 272 75.36764705882354 +2023-06 255 312 81.73076923076923 +2023-07 356 432 82.4074074074074 +2023-08 208 288 72.22222222222223 +2023-09 287 352 81.5340909090909 +2023-10 278 352 78.97727272727273 +2023-11 234 280 83.57142857142857 +2023-12 263 320 82.1875 +2024-01 215 288 74.65277777777777 +2024-02 182 256 71.09375 +2024-03 270 360 75.0 +2024-04 254 296 85.8108108108108 +2024-05 221 288 76.73611111111111 +05/23-05/24 3228 4096 78.80859375 +2024-06 309 368 83.96739130434783 +2024-07 235 344 68.31395348837209 +2024-08 292 528 55.303030303030305 +2024-09 211 376 56.11702127659574 +2024-10 254 424 59.905660377358494 +2024-11 269 456 58.99122807017544 +2024-12 239 392 60.96938775510204 +2025-01 194 408 47.549019607843135 +06/24-01/25 2003 3296 60.77063106796116 +2025-02 203 408 49.754901960784316 +2025-03 306 544 56.25 +2025-04 41 96 42.708333333333336 +v5 1283 2232 57.482078853046595 +v6 726 1400 51.857142857142854 + +AceReason-Nemotron-7B +==================================== +AIME2024 (Avg@64) 68.64583333333334 +AIME2025 (Avg@64) 53.59375000000002 + +AceReason-Nemotron-14B +==================================== +AIME2024 (Avg@64) 78.43749999999997 +AIME2025 (Avg@64) 67.65625 + +AceReason-Nemotron-1.1-7B +==================================== +AIME2024 (Avg@64) 72.60416666666667 +AIME2025 (Avg@64) 64.84375 +``` diff --git a/config.json b/config.json new file mode 100644 index 0000000..4772add --- /dev/null +++ b/config.json @@ -0,0 +1,29 @@ +{ + "architectures": [ + "Qwen2ForCausalLM" + ], + "attention_dropout": 0.0, + "bos_token_id": 151646, + "eos_token_id": 151643, + "hidden_act": "silu", + "hidden_size": 5120, + "initializer_range": 0.02, + "intermediate_size": 13824, + "max_position_embeddings": 131072, + 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"pad_token": { + "content": "<｜end▁of▁sentence｜>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + } +} diff --git a/tokenizer.json b/tokenizer.json new file mode 100644 index 0000000..1a2db24 --- /dev/null +++ b/tokenizer.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e20ddafc659ba90242154b55275402edeca0715e5dbb30f56815a4ce081f4893 +size 11422778 diff --git a/tokenizer_config.json b/tokenizer_config.json new file mode 100644 index 0000000..ef6e98c --- /dev/null +++ b/tokenizer_config.json @@ -0,0 +1,195 @@ +{ + "add_bos_token": true, + "add_eos_token": false, + "add_prefix_space": null, + "added_tokens_decoder": { + "151643": { + "content": "<｜end▁of▁sentence｜>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": true + }, + "151644": { + "content": "<｜User｜>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + 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+ "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151659": { + "content": "<|fim_prefix|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151660": { + "content": "<|fim_middle|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151661": { + "content": "<|fim_suffix|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151662": { + "content": "<|fim_pad|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151663": { + "content": "<|repo_name|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + }, + "151664": { + "content": "<|file_sep|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false, + "special": false + } + }, + "bos_token": "<｜begin▁of▁sentence｜>", + "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '' in content %}{% set content = content.split('')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜>\\n'}}{% endif %}", + "clean_up_tokenization_spaces": false, + "eos_token": "<｜end▁of▁sentence｜>", + "extra_special_tokens": {}, + "legacy": true, + "model_max_length": 16384, + "pad_token": "<｜end▁of▁sentence｜>", + "sp_model_kwargs": {}, + "tokenizer_class": "LlamaTokenizerFast", + "unk_token": null, + "use_default_system_prompt": false +}