OMCHOKSI108/VibeThinker-3B
1
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity

初始化项目，由ModelHub XC社区提供模型

Browse Source 
Model: OMCHOKSI108/VibeThinker-3B
Source: Original Platform
This commit is contained in:
ModelHub XC
2026-06-30 16:22:38 +08:00
commit a5b8dd64ab
24 changed files with 157726 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

42
.gitattributes vendored Normal file
View File

@@ -0,0 +1,42 @@
*.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
pictures/Abstrct.png filter=lfs diff=lfs merge=lfs -text
pictures/Acc_and_Scale.png filter=lfs diff=lfs merge=lfs -text
pictures/Architecture.png filter=lfs diff=lfs merge=lfs -text
pictures/LeetCode.png filter=lfs diff=lfs merge=lfs -text
pictures/VibeThiinker-3B.png filter=lfs diff=lfs merge=lfs -text
pictures/VibeThinker-3B+CLR.png filter=lfs diff=lfs merge=lfs -text

50
ATTRIBUTION.md Normal file
View File

@@ -0,0 +1,50 @@
# Attribution
## Original Sources
| Resource | Link |
|----------|------|
| Original Hugging Face Model | [WeiboAI/VibeThinker-3B](https://huggingface.co/WeiboAI/VibeThinker-3B) |
| Original GitHub Repository | [WeiboAI/VibeThinker](https://github.com/WeiboAI/VibeThinker) |
| Technical Report (3B) | [arXiv:2606.16140](https://arxiv.org/pdf/2606.16140) |
| Technical Report (1.5B) | [arXiv:2511.06221](https://arxiv.org/abs/2511.06221) |
## This Mirror
| Resource | Link |
|----------|------|
| This HF Model | [OMCHOKSI108/VibeThinker-3B](https://huggingface.co/OMCHOKSI108/VibeThinker-3B) |
| GitHub Fork | [OMCHOKSI108/VibeThinkerModel](https://github.com/OMCHOKSI108/VibeThinkerModel) |
## Original Authors
VibeThinker was created by **WeiboAI** and contributors.
**VibeThinker-3B:**
Sen Xu, Shixi Liu, Wei Wang, Jixin Min, Yingwei Dai, Zhibin Yin, Yirong Chen, Xin Zhou, Junlin Zhang
**VibeThinker-1.5B:**
Sen Xu, Yi Zhou, Wei Wang, Jixin Min, Zhibin Yin, Yingwei Dai, Shixi Liu, Lianyu Pang, Yirong Chen, Junlin Zhang
## License
MIT License (inherited from the original). Copyright (c) 2025 WeiboAI.
## Fork Maintainer
- **Om Choksi** — Fork/documentation maintenance
- No claim of ownership or authorship of the original model or code is made.
## Citation
```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},
}
```

50
MODEL_STRUCTURE.md Normal file
View File

@@ -0,0 +1,50 @@
# Model Repository Structure
This document describes the files in this Hugging Face model repository.
```
VibeThinker-3B/
├── README.md # Model card (this fork's documentation)
├── ORIGINAL_README.md # Original model card from WeiboAI (preserved)
├── ATTRIBUTION.md # Credits and license information
├── USAGE.md # Usage and inference guide
├── MODEL_STRUCTURE.md # This file
├── .gitattributes # Git LFS configuration
│
├── config.json # Model configuration (Qwen2.5-Coder-3B based)
├── generation_config.json # Default generation settings
│
├── model-00001-of-00002.safetensors # Model weights (shard 1, ~4.9 GB)
├── model-00002-of-00002.safetensors # Model weights (shard 2, ~1.2 GB)
├── model.safetensors.index.json # Weight index mapping
│
├── tokenizer.json # Full tokenizer (BBPE)
├── tokenizer_config.json # Tokenizer configuration
├── vocab.json # Vocabulary file
├── merges.txt # BPE merges
├── added_tokens.json # Added/special tokens
├── special_tokens_map.json # Special token mappings
│
├── trainer_state.json # Training state metadata
│
└── pictures/ # Figures from the original model card
├── Abstrct.png
├── Acc_and_Scale.png
├── Architecture.png
├── LeetCode.png
├── VibeThiinker-3B.png
└── VibeThinker-3B+CLR.png
```
## File Sizes
| File | Size | Description |
|------|------|-------------|
| model-00001-of-00002.safetensors | ~4.9 GB | First weight shard |
| model-00002-of-00002.safetensors | ~1.2 GB | Second weight shard |
| tokenizer.json | ~11 MB | Full tokenizer |
| vocab.json | ~2.8 MB | Vocabulary |
| merges.txt | ~1.7 MB | BPE merge rules |
| All other files | < 1 MB each | Config, metadata, images |
All `.safetensors` files are tracked with Git LFS.

175
ORIGINAL_README.md Normal file
View File

@@ -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: transformers
---
# VibeThinker-3B
<blockquote style="border-left: 4px solid #ff6b6b; background-color: #fff5f5; padding: 10px 15px; margin: 10px 0; color: #cc3333;">
<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.
For programming tasks, we recommend using this model on competitive programming problems (e.g., <a href="https://leetcode.com/problemset/algorithms/">LeetCode-style</a>).
<br>
<br>
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.
</blockquote>
<p align="center"><a href="https://github.com/WeiboAI/VibeThinker">GitHub</a>&nbsp;&nbsp;|&nbsp;&nbsp;<a href="https://modelscope.cn/models/WeiboAI/VibeThinker-3B">ModelScope</a>&nbsp;&nbsp;|&nbsp;&nbsp;<a href="https://huggingface.co/papers/2606.16140">Technical Report</a></p>
## 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},
}
```

182
README.md Normal file
View File

@@ -0,0 +1,182 @@
---
license: mit
language:
- en
base_model:
- Qwen/Qwen2.5-Coder-3B
tags:
- vibe-thinker
- reasoning
- language-model
- transformers
- pytorch
- documented-fork
- math
- code
- instruction-following
pipeline_tag: text-generation
library_name: transformers
---
# VibeThinker-3B
## Documented Mirror / Fork
**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.**
| Resource | Link |
|----------|------|
| **This Mirror** | [OMCHOKSI108/VibeThinker-3B](https://huggingface.co/OMCHOKSI108/VibeThinker-3B) |
| **Original HF Model** | [WeiboAI/VibeThinker-3B](https://huggingface.co/WeiboAI/VibeThinker-3B) |
| **Original GitHub** | [WeiboAI/VibeThinker](https://github.com/WeiboAI/VibeThinker) |
| **This GitHub Fork** | [OMCHOKSI108/VibeThinkerModel](https://github.com/OMCHOKSI108/VibeThinkerModel) |
| **Technical Report** | [arXiv:2606.16140](https://arxiv.org/pdf/2606.16140) |
| **Original README** | [ORIGINAL_README.md](./ORIGINAL_README.md) (preserved verbatim) |
## Purpose
This is a **documented mirror** of the original VibeThinker-3B model weights for learning, experimentation, and structured usage. It includes:
- Verified copy of the original model weights (unmodified)
- Structured model card with clear attribution
- Usage examples and setup guidance
- Links to the original source and related resources
No model weights have been modified. No additional training or fine-tuning has been performed.
## 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:
- Mathematical reasoning (AIME, HMMT, IMO-AnswerBench)
- Competitive programming (LeetCode, LiveCodeBench)
- STEM reasoning
- Instruction-following with explicit constraints
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>
### 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
View 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
View File

@@ -0,0 +1,26 @@
{
"</think>": 151666,
"</tool_call>": 151658,
"<think>": 151665,
"<tool_call>": 151657,
"<|box_end|>": 151649,
"<|box_start|>": 151648,
"<|endoftext|>": 151643,
"<|file_sep|>": 151664,
"<|fim_middle|>": 151660,
"<|fim_pad|>": 151662,
"<|fim_prefix|>": 151659,
"<|fim_suffix|>": 151661,
"<|im_end|>": 151645,
"<|im_start|>": 151644,
"<|image_pad|>": 151655,
"<|object_ref_end|>": 151647,
"<|object_ref_start|>": 151646,
"<|quad_end|>": 151651,
"<|quad_start|>": 151650,
"<|repo_name|>": 151663,
"<|video_pad|>": 151656,
"<|vision_end|>": 151653,
"<|vision_pad|>": 151654,
"<|vision_start|>": 151652
}

28
config.json Normal file
View 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
View 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
View File

File diff suppressed because it is too large Load Diff

3
model-00001-of-00002.safetensors Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:2f060c748de624b9dcfe3159ef97242810c77974f32f3511668e9c70bef73754
size 4957560304

3
model-00002-of-00002.safetensors Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:f0d4d1ef83d68a9268c42c90ff4d90317b5e60f8f78731349d63cfddc8852ce6
size 1214366696

441
model.safetensors.index.json Normal file
View File

@@ -0,0 +1,441 @@
{
"metadata": {
"total_size": 6171877376
},
"weight_map": {
"model.embed_tokens.weight": "model-00001-of-00002.safetensors",
"model.layers.0.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.0.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.0.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.0.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.0.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.0.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.0.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.0.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.0.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.0.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.0.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.0.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.1.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.1.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.1.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.1.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.1.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.1.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.1.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.1.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.1.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.1.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.1.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.1.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.10.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.10.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.10.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.10.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.10.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.10.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.10.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.10.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.10.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.10.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.10.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.10.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.11.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.11.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.11.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.11.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.11.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.11.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.11.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.11.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.11.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.11.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.11.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.11.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.12.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.12.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.12.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.12.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.12.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.12.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.12.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.12.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.12.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.12.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.12.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.12.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.13.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.13.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.13.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.13.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.13.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.13.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.13.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.13.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.13.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.13.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.13.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.13.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.14.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.14.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.14.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.14.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.14.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.14.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.14.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.14.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.14.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.14.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.14.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.14.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.15.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.15.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.15.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.15.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.15.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.15.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.15.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.15.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.15.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.15.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.15.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.15.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.16.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.16.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.16.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.16.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.16.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.16.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.16.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.16.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.16.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.16.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.16.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.16.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.17.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.17.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.17.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.17.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.17.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.17.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.17.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.17.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.17.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.17.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.17.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.17.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.18.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.18.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.18.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.18.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.18.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.18.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.18.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.18.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.18.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.18.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.18.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.18.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.19.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.19.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.19.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.19.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.19.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.19.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.19.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.19.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.19.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.19.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.19.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.19.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.2.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.2.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.2.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.2.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.2.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.2.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.2.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.2.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.2.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.2.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.2.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.2.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.20.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.20.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.20.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.20.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.20.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.20.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.20.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.20.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.20.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.20.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.20.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.20.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.21.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.21.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.21.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.21.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.21.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.21.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.21.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.21.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.21.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.21.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.21.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.21.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.22.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.22.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.22.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.22.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.22.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.22.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.22.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.22.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.22.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.22.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.22.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.22.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.23.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.23.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.23.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.23.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.23.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.23.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.23.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.23.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.23.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.23.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.23.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.23.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.24.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.24.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.24.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.24.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.24.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.24.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.24.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.24.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.24.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.24.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.24.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.24.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.25.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.25.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.25.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.25.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.25.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.25.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.25.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.25.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.25.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.25.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.25.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.25.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.26.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.26.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.26.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.26.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.26.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.26.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.26.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.26.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.26.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.26.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.26.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.26.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.27.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.27.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.27.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.27.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.27.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.27.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.27.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.27.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.27.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.27.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.27.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.27.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.28.input_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.28.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.28.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.28.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.28.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.28.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.28.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.28.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.28.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.28.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.28.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.28.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.29.input_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.29.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.29.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.29.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.29.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.29.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
"model.layers.29.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.29.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.29.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
"model.layers.29.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.29.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
"model.layers.29.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.3.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.3.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.3.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.3.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.3.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.3.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.3.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.3.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.3.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.3.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.3.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.3.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.30.input_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.30.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.30.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.30.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.30.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.30.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
"model.layers.30.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.30.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.30.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
"model.layers.30.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.30.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
"model.layers.30.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.31.input_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.31.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.31.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.31.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.31.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.31.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
"model.layers.31.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.31.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.31.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
"model.layers.31.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.31.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
"model.layers.31.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.32.input_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.32.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.32.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.32.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.32.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.32.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
"model.layers.32.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.32.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.32.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
"model.layers.32.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.32.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
"model.layers.32.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.33.input_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.33.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.33.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.33.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.33.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.33.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
"model.layers.33.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.33.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.33.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
"model.layers.33.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.33.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
"model.layers.33.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.34.input_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.34.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.34.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.34.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.34.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.34.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
"model.layers.34.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.34.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.34.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
"model.layers.34.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.34.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
"model.layers.34.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.35.input_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.35.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.35.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.35.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.35.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
"model.layers.35.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
"model.layers.35.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.35.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.35.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
"model.layers.35.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.35.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
"model.layers.35.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
"model.layers.4.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.4.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.4.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.4.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.4.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.4.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.4.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.4.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.4.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.4.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.4.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.4.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.5.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.5.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.5.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.5.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.5.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.5.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.5.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.5.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.5.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.5.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.5.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.5.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.6.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.6.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.6.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.6.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.6.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.6.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.6.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.6.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.6.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.6.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.6.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.6.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.7.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.7.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.7.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.7.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.7.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.7.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.7.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.7.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.7.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.7.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.7.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.7.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.8.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.8.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.8.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.8.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.8.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.8.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.8.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.8.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.8.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.8.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.8.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.9.input_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.9.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.9.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.9.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.9.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
"model.layers.9.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.9.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.9.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.9.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.9.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
"model.layers.9.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
"model.norm.weight": "model-00002-of-00002.safetensors"
}
}

3
pictures/Abstrct.png Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:6c800e55c302c62ef138726a1db09fa5d3a7c60e964b4769cedc491bc0dd7254
size 215002

3
pictures/Acc_and_Scale.png Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:e05d10eb97ec6044e03cf4ce1c7eeb94f392e618049490700ea26c994b6978a9
size 103916

3
pictures/Architecture.png Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:71f5f02ff71fe4b47be25c94e0e7dceeb813ce8baf7f3f69d93a613b11ede15e
size 125754

3
pictures/LeetCode.png Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:8d7f23be2515c6be4228983e48697b90a89de0f1b5dbc636e64872f164176a59
size 228922

3
pictures/VibeThiinker-3B.png Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:4ae18631f39b472cfab1f327c659a4ea2c4d111b6e739fc4764709cfbaa49a06
size 248723

3
pictures/VibeThinker-3B+CLR.png Normal file
View 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
View 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
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:296e081e2f5ecf9d87814aa9b0f4b12d670ed2b2e2be6c84e01a9466c953afb7
size 11422267

225
tokenizer_config.json Normal file
View 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
View File

File diff suppressed because it is too large Load Diff

1
vocab.json Normal file
View File

File diff suppressed because one or more lines are too long