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Model: OMCHOKSI108/VibeThinker-3B Source: Original Platform
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ATTRIBUTION.md
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# Attribution
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## Original Sources
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| Resource | Link |
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|----------|------|
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| Original Hugging Face Model | [WeiboAI/VibeThinker-3B](https://huggingface.co/WeiboAI/VibeThinker-3B) |
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| Original GitHub Repository | [WeiboAI/VibeThinker](https://github.com/WeiboAI/VibeThinker) |
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| Technical Report (3B) | [arXiv:2606.16140](https://arxiv.org/pdf/2606.16140) |
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| Technical Report (1.5B) | [arXiv:2511.06221](https://arxiv.org/abs/2511.06221) |
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## This Mirror
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| Resource | Link |
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|----------|------|
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| This HF Model | [OMCHOKSI108/VibeThinker-3B](https://huggingface.co/OMCHOKSI108/VibeThinker-3B) |
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| GitHub Fork | [OMCHOKSI108/VibeThinkerModel](https://github.com/OMCHOKSI108/VibeThinkerModel) |
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## Original Authors
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VibeThinker was created by **WeiboAI** and contributors.
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**VibeThinker-3B:**
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Sen Xu, Shixi Liu, Wei Wang, Jixin Min, Yingwei Dai, Zhibin Yin, Yirong Chen, Xin Zhou, Junlin Zhang
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**VibeThinker-1.5B:**
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Sen Xu, Yi Zhou, Wei Wang, Jixin Min, Zhibin Yin, Yingwei Dai, Shixi Liu, Lianyu Pang, Yirong Chen, Junlin Zhang
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## License
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MIT License (inherited from the original). Copyright (c) 2025 WeiboAI.
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## Fork Maintainer
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- **Om Choksi** — Fork/documentation maintenance
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- No claim of ownership or authorship of the original model or code is made.
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## Citation
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```bibtex
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@misc{xu2026vibethinker3bexploringfrontierverifiable,
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title={VibeThinker-3B: Exploring the Frontier of Verifiable Reasoning in Small Language Models},
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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},
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year={2026},
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eprint={2606.16140},
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archivePrefix={arXiv},
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primaryClass={cs.AI},
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url={https://arxiv.org/abs/2606.16140},
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}
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```
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MODEL_STRUCTURE.md
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MODEL_STRUCTURE.md
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# Model Repository Structure
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This document describes the files in this Hugging Face model repository.
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```
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VibeThinker-3B/
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├── README.md # Model card (this fork's documentation)
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├── ORIGINAL_README.md # Original model card from WeiboAI (preserved)
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├── ATTRIBUTION.md # Credits and license information
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├── USAGE.md # Usage and inference guide
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├── MODEL_STRUCTURE.md # This file
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├── .gitattributes # Git LFS configuration
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│
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├── config.json # Model configuration (Qwen2.5-Coder-3B based)
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├── generation_config.json # Default generation settings
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│
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├── model-00001-of-00002.safetensors # Model weights (shard 1, ~4.9 GB)
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├── model-00002-of-00002.safetensors # Model weights (shard 2, ~1.2 GB)
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├── model.safetensors.index.json # Weight index mapping
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│
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├── tokenizer.json # Full tokenizer (BBPE)
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├── tokenizer_config.json # Tokenizer configuration
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├── vocab.json # Vocabulary file
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├── merges.txt # BPE merges
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├── added_tokens.json # Added/special tokens
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├── special_tokens_map.json # Special token mappings
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│
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├── trainer_state.json # Training state metadata
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│
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└── pictures/ # Figures from the original model card
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├── Abstrct.png
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├── Acc_and_Scale.png
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├── Architecture.png
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├── LeetCode.png
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├── VibeThiinker-3B.png
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└── VibeThinker-3B+CLR.png
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```
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## File Sizes
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| File | Size | Description |
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|------|------|-------------|
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| model-00001-of-00002.safetensors | ~4.9 GB | First weight shard |
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| model-00002-of-00002.safetensors | ~1.2 GB | Second weight shard |
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| tokenizer.json | ~11 MB | Full tokenizer |
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| vocab.json | ~2.8 MB | Vocabulary |
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| merges.txt | ~1.7 MB | BPE merge rules |
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| All other files | < 1 MB each | Config, metadata, images |
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All `.safetensors` files are tracked with Git LFS.
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175
ORIGINAL_README.md
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---
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license: mit
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language:
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- en
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base_model:
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- Qwen/Qwen2.5-Coder-3B
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tags:
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- math
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- code
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- reasoning
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- gpqa
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- instruction-following
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pipeline_tag: text-generation
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library_name: transformers
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---
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# VibeThinker-3B
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<blockquote style="border-left: 4px solid #ff6b6b; background-color: #fff5f5; padding: 10px 15px; margin: 10px 0; color: #cc3333;">
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<span style="font-weight: bold;">🚨 </span> 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.
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For programming tasks, we recommend using this model on competitive programming problems (e.g., <a href="https://leetcode.com/problemset/algorithms/">LeetCode-style</a>).
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<br>
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<br>
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2.For harder math reasoning, try <a href="https://huggingface.co/datasets/meituan-longcat/AMO-Bench">AMOBench</a>, 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.
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</blockquote>
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<p align="center"><a href="https://github.com/WeiboAI/VibeThinker">GitHub</a> | <a href="https://modelscope.cn/models/WeiboAI/VibeThinker-3B">ModelScope</a> | <a href="https://huggingface.co/papers/2606.16140">Technical Report</a></p>
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## Introduction
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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.
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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.
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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.
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## Key Performance Data
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📏 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).
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💡 VibeThinker-3B achieves strong results across mathematics, coding, knowledge, and instruction-following benchmarks.
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🔁 VibeThinker-3B achieves competitive results against first-tier reasoning models and reaches the performance range of top-tier systems on several verifiable reasoning benchmarks.
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🏆 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.
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## Training Pipeline
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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.
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The training pipeline contains the following stages:
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1. **Curriculum-based two-stage SFT**
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- Stage 1 focuses on broad capability coverage across math, code, STEM reasoning, general dialogue, and instruction following.
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- Stage 2 shifts toward harder and longer-horizon reasoning samples.
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- Diversity-Exploring Distillation is used to preserve multiple valid solution paths.
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2. **Multi-domain Reasoning RL**
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- VibeThinker-3B reuses MaxEnt-Guided Policy Optimization (MGPO).
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- RL is applied sequentially to math, code, and STEM reasoning tasks.
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- Training uses a single 64K long-context window to preserve complete long-horizon reasoning trajectories.
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3. **Offline Self-Distillation**
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- High-quality trajectories from Math, Code, and STEM RL checkpoints are filtered and distilled back into a unified student model.
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- A learning-potential score is used to prioritize traces that are correct but not yet well modeled by the student.
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4. **Instruct RL**
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- The final stage improves controllability on user-facing prompts.
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- Rule-based validators and rubric-based reward models are used for format-sensitive and open-ended instruction data.
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## Usage Guidelines
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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.
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For benchmark-style evaluation, the technical report uses vLLM with:
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- `temperature=1.0`
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- `top_p=0.95`
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- `top_k=-1`
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## Quick Start
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Required: **transformers>=4.54.0**
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Recommended for better inference performance: **vLLM==0.10.1 or SGLang>=0.4.9.post6**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
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class VibeThinker:
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def __init__(self, model_path):
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self.model_path = model_path
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self.model = AutoModelForCausalLM.from_pretrained(
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self.model_path,
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low_cpu_mem_usage=True,
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torch_dtype="bfloat16",
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device_map="auto",
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)
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self.tokenizer = AutoTokenizer.from_pretrained(
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self.model_path,
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trust_remote_code=True,
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)
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def infer_text(self, prompt):
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messages = [{"role": "user", "content": prompt}]
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text = self.tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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)
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model_inputs = self.tokenizer([text], return_tensors="pt").to(self.model.device)
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generation_config = dict(
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max_new_tokens=102400,
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do_sample=True,
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temperature=1.0,
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top_p=0.95,
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top_k=None,
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)
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generated_ids = self.model.generate(
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**model_inputs,
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generation_config=GenerationConfig(**generation_config),
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)
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generated_ids = [
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output_ids[len(input_ids):]
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for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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return self.tokenizer.batch_decode(
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generated_ids,
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skip_special_tokens=True,
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)[0]
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if __name__ == "__main__":
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model = VibeThinker("WeiboAI/VibeThinker-3B")
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prompt = "Your Prompt"
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print(model.infer_text(prompt))
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```
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## License
|
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The model repository is licensed under the MIT License.
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## Citations & References
|
||||
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||||
If you use VibeThinker-3B in your research or product, please cite:
|
||||
|
||||
```bibtex
|
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@misc{xu2026vibethinker3bexploringfrontierverifiable,
|
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title={VibeThinker-3B: Exploring the Frontier of Verifiable Reasoning in Small Language Models},
|
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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},
|
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year={2026},
|
||||
eprint={2606.16140},
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archivePrefix={arXiv},
|
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primaryClass={cs.AI},
|
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url={https://arxiv.org/abs/2606.16140},
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}
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```
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182
README.md
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README.md
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---
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license: mit
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language:
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- en
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base_model:
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- Qwen/Qwen2.5-Coder-3B
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tags:
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- vibe-thinker
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- reasoning
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- language-model
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- transformers
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- pytorch
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- documented-fork
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- math
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- code
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||||
- instruction-following
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pipeline_tag: text-generation
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library_name: transformers
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---
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# VibeThinker-3B
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## Documented Mirror / Fork
|
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**This repository is a documented mirror/fork of the original [VibeThinker-3B](https://huggingface.co/WeiboAI/VibeThinker-3B) model. Original model credits belong to WeiboAI and contributors.**
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|
||||
| Resource | Link |
|
||||
|----------|------|
|
||||
| **This Mirror** | [OMCHOKSI108/VibeThinker-3B](https://huggingface.co/OMCHOKSI108/VibeThinker-3B) |
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||||
| **Original HF Model** | [WeiboAI/VibeThinker-3B](https://huggingface.co/WeiboAI/VibeThinker-3B) |
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||||
| **Original GitHub** | [WeiboAI/VibeThinker](https://github.com/WeiboAI/VibeThinker) |
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| **This GitHub Fork** | [OMCHOKSI108/VibeThinkerModel](https://github.com/OMCHOKSI108/VibeThinkerModel) |
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||||
| **Technical Report** | [arXiv:2606.16140](https://arxiv.org/pdf/2606.16140) |
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| **Original README** | [ORIGINAL_README.md](./ORIGINAL_README.md) (preserved verbatim) |
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## Purpose
|
||||
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This is a **documented mirror** of the original VibeThinker-3B model weights for learning, experimentation, and structured usage. It includes:
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- Verified copy of the original model weights (unmodified)
|
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- Structured model card with clear attribution
|
||||
- Usage examples and setup guidance
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||||
- Links to the original source and related resources
|
||||
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||||
No model weights have been modified. No additional training or fine-tuning has been performed.
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## Model Description
|
||||
|
||||
VibeThinker-3B is a 3-billion-parameter dense reasoning model developed by WeiboAI. It is built upon Qwen2.5-Coder-3B and post-trained with an upgraded Spectrum-to-Signal (SSP) pipeline. The model is designed for tasks with reliable verification signals, including:
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||||
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||||
- Mathematical reasoning (AIME, HMMT, IMO-AnswerBench)
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||||
- Competitive programming (LeetCode, LiveCodeBench)
|
||||
- STEM reasoning
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||||
- Instruction-following with explicit constraints
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||||
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||||
The technical report shows that VibeThinker-3B can reach frontier-level performance on several verifiable reasoning benchmarks while remaining much smaller than typical frontier reasoning systems.
|
||||
|
||||
<p align="center"><img src="./pictures/Abstrct.png" width="80%"/></p>
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||||
|
||||
### Key Performance
|
||||
|
||||
- **Ultra-Efficient Frontier-Level Reasoning**: With only **3B parameters**, VibeThinker-3B approaches the performance range of much larger frontier reasoning systems. It matches or closely trails models that are orders of magnitude larger on challenging reasoning benchmarks, demonstrating that compact models can encode high-density reasoning ability when trained with reliable verifiable signals.
|
||||
|
||||
<p align="center"><img src="./pictures/Acc_and_Scale.png" width="80%"/></p>
|
||||
|
||||
- **Outstanding Capabilities Across Benchmarks**: VibeThinker-3B delivers strong and balanced performance across mathematics, coding, and out-of-distribution evaluation. It achieves **94.3** on AIME26, **89.3** on HMMT25, **80.2 Pass@1** on LiveCodeBench v6, and a **96.1%** acceptance rate on recent unseen LeetCode weekly and biweekly contests from Apr. 25 to May 31, 2026.
|
||||
|
||||
<p align="center"><img src="./pictures/VibeThiinker-3B.png" width="80%"/></p>
|
||||
|
||||
- **Inference-Time Scaling with CLR**: VibeThinker-3B introduces Claim-Level Reliability Assessment (CLR), a test-time scaling strategy for answer-verifiable reasoning. CLR further boosts performance on math benchmarks, raising AIME26 from **94.3** to **97.1**, HMMT25 from **89.3** to **95.4**, and BruMO25 to **99.2**.
|
||||
|
||||
<p align="center"><img src="./pictures/VibeThinker-3B+CLR.png" width="80%"/></p>
|
||||
|
||||
- **Out-of-Distribution Performance**: 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.
|
||||
|
||||
<p align="center"><img src="./pictures/LeetCode.png" width="80%"/></p>
|
||||
|
||||
### 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.
|
||||
|
||||
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.
|
||||
|
||||
<p align="center"><img src="./pictures/Architecture.png" width="80%"/></p>
|
||||
|
||||
For full details, see the [original model card](./ORIGINAL_README.md) and the [technical report](https://arxiv.org/pdf/2606.16140).
|
||||
|
||||
## Installation
|
||||
|
||||
```bash
|
||||
pip install transformers>=4.54.0
|
||||
```
|
||||
|
||||
For better inference performance:
|
||||
|
||||
```bash
|
||||
pip install vllm==0.10.1
|
||||
# or
|
||||
pip install sglang>=0.4.9.post6
|
||||
```
|
||||
|
||||
## Loading the Model
|
||||
|
||||
```python
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
"WeiboAI/VibeThinker-3B", # or "OMCHOKSI108/VibeThinker-3B"
|
||||
low_cpu_mem_usage=True,
|
||||
torch_dtype="bfloat16",
|
||||
device_map="auto",
|
||||
)
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
"WeiboAI/VibeThinker-3B",
|
||||
trust_remote_code=True,
|
||||
)
|
||||
```
|
||||
|
||||
## Inference Example
|
||||
|
||||
```python
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
|
||||
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
"OMCHOKSI108/VibeThinker-3B",
|
||||
low_cpu_mem_usage=True,
|
||||
torch_dtype="bfloat16",
|
||||
device_map="auto",
|
||||
)
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
"OMCHOKSI108/VibeThinker-3B",
|
||||
trust_remote_code=True,
|
||||
)
|
||||
|
||||
messages = [{"role": "user", "content": "What is the sum of the first 100 prime numbers?"}]
|
||||
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
|
||||
inputs = tokenizer([text], return_tensors="pt").to(model.device)
|
||||
|
||||
outputs = model.generate(
|
||||
**inputs,
|
||||
generation_config=GenerationConfig(
|
||||
max_new_tokens=40960,
|
||||
do_sample=True,
|
||||
temperature=0.6,
|
||||
top_p=0.95,
|
||||
top_k=None,
|
||||
),
|
||||
)
|
||||
response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
|
||||
print(response)
|
||||
```
|
||||
|
||||
## Hardware Notes
|
||||
|
||||
| Precision | Min VRAM | Recommended GPU |
|
||||
|-----------|----------|----------------|
|
||||
| bfloat16 | ~8 GB | RTX 3070+ / A10G+ |
|
||||
| float32 | ~16 GB | A100+ |
|
||||
|
||||
## Limitations
|
||||
|
||||
- This model was not trained on tool-calling or agent-based programming data. It is not recommended for function calling, API orchestration, or autonomous coding agents.
|
||||
- For open-domain knowledge tasks, larger general-purpose models may be more suitable.
|
||||
- This is a mirror — no additional training or fine-tuning has been performed by the maintainer.
|
||||
|
||||
## Attribution
|
||||
|
||||
Original model credits belong to **WeiboAI and contributors**.
|
||||
|
||||
- **Original Authors (VibeThinker-3B):** Sen Xu, Shixi Liu, Wei Wang, Jixin Min, Yingwei Dai, Zhibin Yin, Yirong Chen, Xin Zhou, Junlin Zhang
|
||||
- **Original Authors (VibeThinker-1.5B):** Sen Xu, Yi Zhou, Wei Wang, Jixin Min, Zhibin Yin, Yingwei Dai, Shixi Liu, Lianyu Pang, Yirong Chen, Junlin Zhang
|
||||
- **Fork/Documentation Maintainer:** Om Choksi
|
||||
|
||||
See [ATTRIBUTION.md](./ATTRIBUTION.md) for full details.
|
||||
|
||||
## License
|
||||
|
||||
The model repository is licensed under the **MIT License** (inherited from the original).
|
||||
92
USAGE.md
Normal file
92
USAGE.md
Normal file
@@ -0,0 +1,92 @@
|
||||
# Usage Guide
|
||||
|
||||
## Requirements
|
||||
|
||||
- Python 3.10+
|
||||
- transformers >= 4.54.0
|
||||
- CUDA-capable GPU with 8GB+ VRAM (for bfloat16 inference)
|
||||
|
||||
## Installation
|
||||
|
||||
```bash
|
||||
pip install transformers>=4.54.0 torch
|
||||
```
|
||||
|
||||
For better performance with vLLM:
|
||||
|
||||
```bash
|
||||
pip install vllm==0.10.1
|
||||
```
|
||||
|
||||
## Loading the Model
|
||||
|
||||
```python
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
"OMCHOKSI108/VibeThinker-3B",
|
||||
low_cpu_mem_usage=True,
|
||||
torch_dtype="bfloat16",
|
||||
device_map="auto",
|
||||
)
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
"OMCHOKSI108/VibeThinker-3B",
|
||||
trust_remote_code=True,
|
||||
)
|
||||
```
|
||||
|
||||
## Basic Inference
|
||||
|
||||
```python
|
||||
from transformers import GenerationConfig
|
||||
|
||||
messages = [{"role": "user", "content": "Solve for x: 3x + 7 = 22"}]
|
||||
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
|
||||
inputs = tokenizer([text], return_tensors="pt").to(model.device)
|
||||
|
||||
outputs = model.generate(
|
||||
**inputs,
|
||||
generation_config=GenerationConfig(
|
||||
max_new_tokens=40960,
|
||||
do_sample=True,
|
||||
temperature=0.6,
|
||||
top_p=0.95,
|
||||
top_k=None,
|
||||
),
|
||||
)
|
||||
response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
|
||||
print(response)
|
||||
```
|
||||
|
||||
## Recommended Parameters
|
||||
|
||||
| Parameter | Value | Notes |
|
||||
|-----------|-------|-------|
|
||||
| temperature | 0.6 or 1.0 | Lower for focused answers, higher for diversity |
|
||||
| top_p | 0.95 | Nucleus sampling threshold |
|
||||
| top_k | None (or -1 in vLLM/SGLang) | Skip top-k filtering |
|
||||
| max_new_tokens | 40960-102400 | Longer for complex reasoning tasks |
|
||||
|
||||
## Using with vLLM
|
||||
|
||||
```python
|
||||
from vllm import LLM, SamplingParams
|
||||
|
||||
llm = LLM("OMCHOKSI108/VibeThinker-3B", dtype="bfloat16")
|
||||
params = SamplingParams(temperature=0.6, top_p=0.95, top_k=-1, max_tokens=40960)
|
||||
output = llm.generate("What is the derivative of x^3?", params)
|
||||
print(output[0].outputs[0].text)
|
||||
```
|
||||
|
||||
## Hardware Requirements
|
||||
|
||||
- **Minimum:** 8 GB VRAM (bfloat16, 3B model)
|
||||
- **Recommended:** 16 GB+ VRAM (allows larger batch sizes or longer sequences)
|
||||
- **CPU inference:** Possible but significantly slower
|
||||
|
||||
## Important Notes
|
||||
|
||||
1. This model is optimized for verifiable reasoning tasks (math, code, STEM).
|
||||
2. It is not designed for tool-calling, agent-based programming, or function calling.
|
||||
3. For open-domain knowledge tasks, larger models may be more suitable.
|
||||
4. See the [original model card](./ORIGINAL_README.md) for further guidance from the authors.
|
||||
26
added_tokens.json
Normal file
26
added_tokens.json
Normal file
@@ -0,0 +1,26 @@
|
||||
{
|
||||
"</think>": 151666,
|
||||
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|
||||
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|
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|
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"<|endoftext|>": 151643,
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|
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|
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"<|object_ref_end|>": 151647,
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|
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"<|quad_end|>": 151651,
|
||||
"<|quad_start|>": 151650,
|
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"<|repo_name|>": 151663,
|
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"<|video_pad|>": 151656,
|
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"<|vision_end|>": 151653,
|
||||
"<|vision_pad|>": 151654,
|
||||
"<|vision_start|>": 151652
|
||||
}
|
||||
28
config.json
Normal file
28
config.json
Normal file
@@ -0,0 +1,28 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen2ForCausalLM"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151643,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2048,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 11008,
|
||||
"max_position_embeddings": 131072,
|
||||
"max_window_layers": 36,
|
||||
"model_type": "qwen2",
|
||||
"num_attention_heads": 16,
|
||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 2,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000.0,
|
||||
"sliding_window": 32768,
|
||||
"tie_word_embeddings": true,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.51.3",
|
||||
"use_cache": false,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
||||
6
generation_config.json
Normal file
6
generation_config.json
Normal file
@@ -0,0 +1,6 @@
|
||||
{
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151643,
|
||||
"max_new_tokens": 65536,
|
||||
"transformers_version": "4.51.3"
|
||||
}
|
||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00002.safetensors
Normal file
3
model-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
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||||
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|
||||
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model-00002-of-00002.safetensors
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441
model.safetensors.index.json
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441
model.safetensors.index.json
Normal file
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|
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{
|
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||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:4ae18631f39b472cfab1f327c659a4ea2c4d111b6e739fc4764709cfbaa49a06
|
||||
size 248723
|
||||
3
pictures/VibeThinker-3B+CLR.png
Normal file
3
pictures/VibeThinker-3B+CLR.png
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9ce66aa2fb3c5c93f1b8c7746c65d2217aedce19218d96f10b16092c0bfe0508
|
||||
size 213442
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:296e081e2f5ecf9d87814aa9b0f4b12d670ed2b2e2be6c84e01a9466c953afb7
|
||||
size 11422267
|
||||
225
tokenizer_config.json
Normal file
225
tokenizer_config.json
Normal file
@@ -0,0 +1,225 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"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
|
||||
},
|
||||
"151665": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151666": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\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 <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "right",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
4962
trainer_state.json
Normal file
4962
trainer_state.json
Normal file
File diff suppressed because it is too large
Load Diff
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user