From 22ce435fafabb5a636b4dff7cd9c1e274ab35550 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sat, 15 Aug 2026 01:25:16 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: Basher17/VibeThinker-3B-MLX Source: Original Platform --- .gitattributes | 36 +++ README.md | 175 ++++++++++++ chat_template.jinja | 54 ++++ config.json | 28 ++ generation_config.json | 6 + model-00001-of-00002.safetensors | 3 + model-00002-of-00002.safetensors | 3 + model.safetensors.index.json | 442 +++++++++++++++++++++++++++++++ tokenizer.json | 3 + tokenizer_config.json | 17 ++ 10 files changed, 767 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 chat_template.jinja create mode 100644 config.json create mode 100644 generation_config.json create mode 100644 model-00001-of-00002.safetensors create mode 100644 model-00002-of-00002.safetensors create mode 100644 model.safetensors.index.json create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.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 +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..c894d37 --- /dev/null +++ b/README.md @@ -0,0 +1,175 @@ +--- +license: mit +language: +- en +base_model: +- Qwen/Qwen2.5-Coder-3B +tags: +- math +- code +- reasoning +- gpqa +- instruction-following +pipeline_tag: text-generation +library_name: mlx +--- + +# VibeThinker-3B + +
+ 🚨 1.This model was not trained on tool-calling or agent-based programming data. We therefore do not recommend using it for tasks that involve function calling, API orchestration, or autonomous coding agents. +For programming tasks, we recommend using this model on competitive programming problems (e.g., LeetCode-style). + +
+
+ 2.For harder math reasoning, try AMOBench, a problem set harder than the International Mathematical Olympiad (IMO), with included standard answers. Use it to evaluate VibeThinker against other SOTA models. Note: due to extreme difficulty, set max tokens to 60K–100K. + +
+ +

GitHub  |  ModelScope  |  Technical Report

+ +## Introduction + +VibeThinker-3B is a further exploration of the VibeThinker series at the 3B-parameter scale, focusing on challenging reasoning tasks with clear verification signals, such as mathematics, coding, and STEM. By systematically optimizing the Spectrum-to-Signal Principle (SSP) post-training pipeline introduced in VibeThinker-1.5B, VibeThinker-3B achieves strong performance on AIME, HMMT, IMO-AnswerBench, LiveCodeBench, and recent LeetCode contests, reaching the performance range of top-tier frontier reasoning models, including Qwen3.6 Plus, Gemini 3 Pro, GLM-5, and Kimi K2.5, on verifiable reasoning benchmarks. + +Motivated by these observations, we propose the Parametric Compression-Coverage Hypothesis: different capabilities depend on model parameters in fundamentally different ways. Verifiable reasoning is closer to a highly compressible, parameter-dense capability, centered on multi-step reasoning, constraint satisfaction, self-correction, and answer verification. When the task space is sufficiently structured and feedback signals are sufficiently reliable, compact models may also carry near-frontier reasoning capabilities. In contrast, open-domain knowledge, general-purpose dialogue, and long-tail scenario understanding rely more heavily on large-scale parameters to broadly cover facts, concepts, and world knowledge. + +From VibeThinker-1.5B to VibeThinker-3B, our goal is not to build a small model that replaces large-scale models, but to examine the real boundaries of small models along specific capability dimensions. With VibeThinker-3B, we aim to show that small models should not be viewed merely as a compromise for reducing deployment costs. For capability domains with clear feedback and verification mechanisms, SLMs emerge as a promising research trajectory toward frontier-level performance that is fundamentally complementary to the traditional parameter scaling paradigm. + +![alt text](pictures/Abstrct.png) + +## Key Performance Data + +📏 In terms of reasoning accuracy relative to model scale, VibeThinker-3B reaches 76.4 on IMO-AnswerBench, a highly challenging benchmark with 400 IMO-level problems, with only 3B parameters, and improves to 80.6 with Claim-Level Reliability Assessment (CLR), a test-time scaling strategy for answer-verifiable reasoning tasks. This demonstrates that a model within a strictly small-model regime can reach the performance range of substantially larger models, such as DeepSeek V3.2 (78.3, 671B), GLM-5 (82.5, 744B), and Kimi K2.5 (81.8, 1T). + +![alt text](pictures/Acc_and_Scale.png) + +💡 VibeThinker-3B achieves strong results across mathematics, coding, knowledge, and instruction-following benchmarks. + +![alt text](pictures/VibeThiinker-3B.png) + +🔁 VibeThinker-3B achieves competitive results against first-tier reasoning models and reaches the performance range of top-tier systems on several verifiable reasoning benchmarks. + +![alt text](pictures/VibeThinker-3B+CLR.png) + +🏆 To further test the model's out-of-distribution performance, we evaluate VibeThinker-3B on recent unseen LeetCode weekly and biweekly contests (Python) from Apr. 25 to May 31, 2026. VibeThinker-3B passes **123/128** first-attempt submissions, corresponding to a **96.1%** acceptance rate. + +![alt text](pictures/LeetCode.png) + + +## Training Pipeline + +VibeThinker-3B follows the **Spectrum-to-Signal Principle (SSP)** introduced in VibeThinker-1.5B. The SFT stage constructs a broad spectrum of valid reasoning trajectories, while the RL stage amplifies correct reasoning signals using verifiable rewards. + +![alt text](pictures/Architecture.png) + +The training pipeline contains the following stages: + +1. **Curriculum-based two-stage SFT** + - Stage 1 focuses on broad capability coverage across math, code, STEM reasoning, general dialogue, and instruction following. + - Stage 2 shifts toward harder and longer-horizon reasoning samples. + - Diversity-Exploring Distillation is used to preserve multiple valid solution paths. + +2. **Multi-domain Reasoning RL** + - VibeThinker-3B reuses MaxEnt-Guided Policy Optimization (MGPO). + - RL is applied sequentially to math, code, and STEM reasoning tasks. + - Training uses a single 64K long-context window to preserve complete long-horizon reasoning trajectories. + +3. **Offline Self-Distillation** + - High-quality trajectories from Math, Code, and STEM RL checkpoints are filtered and distilled back into a unified student model. + - A learning-potential score is used to prioritize traces that are correct but not yet well modeled by the student. + +4. **Instruct RL** + - The final stage improves controllability on user-facing prompts. + - Rule-based validators and rubric-based reward models are used for format-sensitive and open-ended instruction data. + +## Usage Guidelines + +We recommend using VibeThinker-3B for competitive-style math, coding, STEM reasoning, and other tasks where the target answer can be verified. For broad open-domain knowledge tasks, larger general-purpose models may still be more suitable. + +For benchmark-style evaluation, the technical report uses vLLM with: + +- `temperature=1.0` +- `top_p=0.95` +- `top_k=-1` + +## Quick Start + +Required: **transformers>=4.54.0** + +Recommended for better inference performance: **vLLM==0.10.1 or SGLang>=0.4.9.post6** + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig + + +class VibeThinker: + def __init__(self, model_path): + self.model_path = model_path + self.model = AutoModelForCausalLM.from_pretrained( + self.model_path, + low_cpu_mem_usage=True, + torch_dtype="bfloat16", + device_map="auto", + ) + self.tokenizer = AutoTokenizer.from_pretrained( + self.model_path, + trust_remote_code=True, + ) + + def infer_text(self, prompt): + messages = [{"role": "user", "content": prompt}] + text = self.tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, + ) + model_inputs = self.tokenizer([text], return_tensors="pt").to(self.model.device) + + generation_config = dict( + max_new_tokens=102400, + do_sample=True, + temperature=1.0, + top_p=0.95, + top_k=None, + ) + generated_ids = self.model.generate( + **model_inputs, + generation_config=GenerationConfig(**generation_config), + ) + generated_ids = [ + output_ids[len(input_ids):] + for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) + ] + + return self.tokenizer.batch_decode( + generated_ids, + skip_special_tokens=True, + )[0] + + +if __name__ == "__main__": + model = VibeThinker("WeiboAI/VibeThinker-3B") + prompt = "Your Prompt" + print(model.infer_text(prompt)) +``` + +## License + +The model repository is licensed under the MIT License. + +## Citations & References + +If you use VibeThinker-3B in your research or product, please cite: + +```bibtex +@misc{xu2026vibethinker3bexploringfrontierverifiable, + title={VibeThinker-3B: Exploring the Frontier of Verifiable Reasoning in Small Language Models}, + author={Sen Xu and Shixi Liu and Wei Wang and Jixin Min and Yingwei Dai and Zhibin Yin and Yirong Chen and Xin Zhou and Junlin Zhang}, + year={2026}, + eprint={2606.16140}, + archivePrefix={arXiv}, + primaryClass={cs.AI}, + url={https://arxiv.org/abs/2606.16140}, +} +``` \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..28028c0 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,54 @@ +{%- if tools %} + {{- '<|im_start|>system\n' }} + {%- if messages[0]['role'] == 'system' %} + {{- messages[0]['content'] }} + {%- else %} + {{- 'You are a helpful assistant.' }} + {%- endif %} + {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n" }} + {%- for tool in tools %} + {{- "\n" }} + {{- tool | tojson }} + {%- endfor %} + {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|im_end|>\n" }} +{%- else %} + {%- if messages[0]['role'] == 'system' %} + {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }} + {%- else %} + {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }} + {%- endif %} +{%- endif %} +{%- for message in messages %} + {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %} + {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }} + {%- elif message.role == "assistant" %} + {{- '<|im_start|>' + message.role }} + {%- if message.content %} + {{- '\n' + message.content }} + {%- endif %} + {%- for tool_call in message.tool_calls %} + {%- if tool_call.function is defined %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {{- 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