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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

.ipynb_checkpoints/README-checkpoint.md

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+---
+library_name: transformers
+license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507/blob/main/LICENSE
+pipeline_tag: text-generation
+tags:
+- AWQ
+- 量化修复
+- vLLM
+base_model:
+  - Kwaipilot/KAT-V1-40B
+base_model_relation: quantized
+---
+# KAT-V1-40B-AWQ
+Base model: [Kwaipilot/KAT-V1-40B](https://huggingface.co/Kwaipilot/KAT-V1-40B)
+
+
+### 【vLLM Single Node with 4 GPUs Startup Command】
+```
+CONTEXT_LENGTH=32768
+
+vllm serve \
+    QuantTrio/KAT-V1-40B-AWQ \
+    --served-model-name KAT-V1-40B-AWQ \
+    --swap-space 16 \
+    --max-num-seqs 512 \
+    --max-model-len $CONTEXT_LENGTH \
+    --max-seq-len-to-capture $CONTEXT_LENGTH \
+    --gpu-memory-utilization 0.9 \
+    --tensor-parallel-size 4 \
+    --trust-remote-code \
+    --disable-log-requests \
+    --host 0.0.0.0 \
+    --port 8000
+```
+
+### 【Dependencies】
+
+```
+vllm==0.10.0
+```
+
+### 【Model Update Date】
+``` 
+2025-07-31
+1. fast commit
+```
+
+### 【Model Files】
+| File Size   | Last Updated       |
+|--------|--------------|
+| `22GB` | `2025-07-31` |
+
+
+### 【Model Download】
+
+```python
+from huggingface_hub  import snapshot_download
+snapshot_download('QuantTrio/KAT-V1-40B-AWQ', cache_dir="your_local_path")
+```
+
+### 【Overview】
+<div align="center">
+  <img src="https://raw.githubusercontent.com/Anditty/OASIS/refs/heads/main/Group.svg" width="60%" alt="Kwaipilot" />
+</div>
+
+<hr>
+
+<div align="center" style="line-height: 1;">
+  <a href="https://huggingface.co/Kwaipilot/KAT-V1-40B" target="_blank">
+    <img alt="Hugging Face" src="https://img.shields.io/badge/HuggingFace-fcd022?style=for-the-badge&logo=huggingface&logoColor=000&labelColor"/>
+  </a>
+  
+  <a href="https://arxiv.org/pdf/2507.08297" target="_blank">
+    <img alt="arXiv" src="https://img.shields.io/badge/arXiv-2507.08297-b31b1b.svg?style=for-the-badge"/>
+  </a>
+</div>
+
+# News
+
+- Kwaipilot-AutoThink ranks first among all open-source models on [LiveCodeBench Pro](https://livecodebenchpro.com/), a challenging benchmark explicitly designed to prevent data leakage, and even surpasses strong proprietary systems such as Seed and o3-mini.
+
+***
+
+# Introduction
+
+**KAT (Kwaipilot-AutoThink)** is an open-source large-language model that mitigates *over-thinking* by learning **when** to produce explicit chain-of-thought and **when** to answer directly. 
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/61ee40a269351366e29972ad/zdnsvBmv6hWIC2Qxxy1fD.png)
+
+Its development follows a concise two-stage training pipeline:
+
+<table>
+  <thead>
+    <tr>
+      <th style="text-align:left; width:18%;">Stage</th>
+      <th style="text-align:left;">Core Idea</th>
+      <th style="text-align:left;">Key Techniques</th>
+      <th style="text-align:left;">Outcome</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><strong>1. Pre-training</strong></td>
+      <td>Inject knowledge while separating “reasoning” from “direct answering”.</td>
+      <td>
+        <em>Dual-regime data</em><br>
+        • <strong>Think-off</strong> queries labeled via a custom tagging system.<br>
+        • <strong>Think-on</strong> queries generated by a multi-agent solver.<br><br>
+        <em>Knowledge Distillation&nbsp;+&nbsp;Multi-Token Prediction</em> for fine-grained utility.
+      </td>
+      <td>Base model attains strong factual and reasoning skills without full-scale pre-training costs.</td>
+    </tr>
+    <tr>
+      <td><strong>2. Post-training</strong></td>
+      <td>Make reasoning optional and efficient.</td>
+      <td>
+        <em>Cold-start AutoThink</em> — majority vote sets the initial thinking mode.<br>
+        <em>Step-SRPO</em> — intermediate supervision rewards correct <strong>mode selection</strong> and <strong>answer accuracy</strong> under that mode.
+      </td>
+      <td>Model triggers CoT only when beneficial, reducing token use and speeding inference.</td>
+    </tr>
+  </tbody>
+</table>
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/61ee40a269351366e29972ad/cwFAEh7Rl3f4FU46z8gBZ.png)
+
+
+***
+
+# Data Format
+
+
+KAT produces responses in a **structured template** that makes the reasoning path explicit and machine-parsable.  
+Two modes are supported:
+
+
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61ee40a269351366e29972ad/H8iAvQMMT02nyvlYnI5q1.jpeg)
+
+
+## Special Tokens
+
+| Token | Description |
+|-------|-------------|
+| `<judge>` | Analyzes the input to decide whether explicit reasoning is needed. |
+| `<think_on>` / `<think_off>` | Indicates whether reasoning is **activated** (“on”) or **skipped** (“off”). |
+| `<think>` | Marks the start of the chain-of-thought segment when `think_on` is chosen. |
+| `<answer>` | Marks the start of the final user-facing answer. |
+
+
+***
+
+# 🔧 Quick Start
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "Kwaipilot/KAT-V1-40B"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+# prepare the model input
+prompt = "Give me a short introduction to large language model."
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=65536,
+    temperature=0.6,
+    top_p=0.95,
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 
+content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")
+print("prompt:\n", prompt)
+print("content:\n", content)
+"""
+prompt:
+Give me a short introduction to large language model.
+content:
+<judge>
+The user's request is to provide a concise factual introduction to large language models, which involves retrieving and summarizing basic information. This task is straightforward as it only requires recalling and presenting well-known details without deeper analysis. No complex reasoning is needed here—just a simple explanation will suffice.
+</judge>
+
+<think_off>
+<answer>
+A **Large Language Model (LLM)** is an advanced AI system trained on vast amounts of text data to understand, generate, and process human-like language. Here’s a concise introduction:
+
+### Key Points:
+1. **Training**: Trained on diverse text sources (books, websites, etc.) using deep learning.
+2. **Capabilities**: 
+   - Answer questions, generate text, summarize content, translate languages.
+   - Understand context, sentiment, and nuances in language.
+3. **Architecture**: Often based on **transformer models** (e.g., BERT, GPT, LLaMA).
+4. **Scale**: Billions of parameters, requiring massive computational resources.
+5. **Applications**: Chatbots, content creation, coding assistance, research, and more.
+
+### Examples:
+- **OpenAI’s GPT-4**: Powers ChatGPT.
+- **Google’s Gemini**: Used in Bard.
+- **Meta’s LLaMA**: Open-source alternative.
+
+### Challenges:
+- **Bias**: Can reflect biases in training data.
+- **Accuracy**: May hallucinate "facts" not grounded in reality.
+- **Ethics**: Raises concerns about misinformation and job displacement.
+
+LLMs represent a leap forward in natural language processing, enabling machines to interact with humans in increasingly sophisticated ways. 🌐🤖
+</answer>
+"""
+```
+
+***
+
+# Future Releases
+
+Looking ahead, we will publish a companion paper that fully documents the **AutoThink training framework**, covering:
+
+* Cold-start initialization procedures  
+* Reinforcement-learning (Step-SRPO) strategies  
+* Data curation and reward design details  
+
+At the same time, we will open-source:
+
+* **Training resources** – the curated dual-regime datasets and RL codebase  
+* **Model suite** – checkpoints at 1.5B, 7B, and 13B parameters, all trained with AutoThink gating  

.mv

@@ -0,0 +1 @@
+Revision:master,CreatedAt:1753930441

LICENSE

@@ -0,0 +1,57 @@
+MODEL LICENSE AGREEMENT
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+b.   Any disputes arising from or related to this Agreement shall be under the jurisdiction of the People's Court where the Licensor is located. 
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+
+Use Restrictions
+
+You agree not to use the Model or Derivatives of the Model:
+
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+-    To generate or disseminate verifiably false information and/or content with the purpose of harming others; 
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+-    For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories.

README.md

@@ -0,0 +1,239 @@
+---
+library_name: transformers
+license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507/blob/main/LICENSE
+pipeline_tag: text-generation
+tags:
+- AWQ
+- 量化修复
+- vLLM
+base_model:
+  - Kwaipilot/KAT-V1-40B
+base_model_relation: quantized
+---
+# KAT-V1-40B-AWQ
+Base model: [Kwaipilot/KAT-V1-40B](https://huggingface.co/Kwaipilot/KAT-V1-40B)
+
+
+### 【vLLM Single Node with 4 GPUs Startup Command】
+```
+CONTEXT_LENGTH=32768
+
+vllm serve \
+    QuantTrio/KAT-V1-40B-AWQ \
+    --served-model-name KAT-V1-40B-AWQ \
+    --swap-space 16 \
+    --max-num-seqs 512 \
+    --max-model-len $CONTEXT_LENGTH \
+    --max-seq-len-to-capture $CONTEXT_LENGTH \
+    --gpu-memory-utilization 0.9 \
+    --tensor-parallel-size 4 \
+    --trust-remote-code \
+    --disable-log-requests \
+    --host 0.0.0.0 \
+    --port 8000
+```
+
+### 【Dependencies】
+
+```
+vllm==0.10.0
+```
+
+### 【Model Update Date】
+``` 
+2025-07-31
+1. fast commit
+```
+
+### 【Model Files】
+| File Size   | Last Updated       |
+|--------|--------------|
+| `22GB` | `2025-07-31` |
+
+
+### 【Model Download】
+
+```python
+from huggingface_hub  import snapshot_download
+snapshot_download('QuantTrio/KAT-V1-40B-AWQ', cache_dir="your_local_path")
+```
+
+### 【Overview】
+<div align="center">
+  <img src="https://raw.githubusercontent.com/Anditty/OASIS/refs/heads/main/Group.svg" width="60%" alt="Kwaipilot" />
+</div>
+
+<hr>
+
+<div align="center" style="line-height: 1;">
+  <a href="https://huggingface.co/Kwaipilot/KAT-V1-40B" target="_blank">
+    <img alt="Hugging Face" src="https://img.shields.io/badge/HuggingFace-fcd022?style=for-the-badge&logo=huggingface&logoColor=000&labelColor"/>
+  </a>
+  
+  <a href="https://arxiv.org/pdf/2507.08297" target="_blank">
+    <img alt="arXiv" src="https://img.shields.io/badge/arXiv-2507.08297-b31b1b.svg?style=for-the-badge"/>
+  </a>
+</div>
+
+# News
+
+- Kwaipilot-AutoThink ranks first among all open-source models on [LiveCodeBench Pro](https://livecodebenchpro.com/), a challenging benchmark explicitly designed to prevent data leakage, and even surpasses strong proprietary systems such as Seed and o3-mini.
+
+***
+
+# Introduction
+
+**KAT (Kwaipilot-AutoThink)** is an open-source large-language model that mitigates *over-thinking* by learning **when** to produce explicit chain-of-thought and **when** to answer directly. 
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/61ee40a269351366e29972ad/zdnsvBmv6hWIC2Qxxy1fD.png)
+
+Its development follows a concise two-stage training pipeline:
+
+<table>
+  <thead>
+    <tr>
+      <th style="text-align:left; width:18%;">Stage</th>
+      <th style="text-align:left;">Core Idea</th>
+      <th style="text-align:left;">Key Techniques</th>
+      <th style="text-align:left;">Outcome</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><strong>1. Pre-training</strong></td>
+      <td>Inject knowledge while separating “reasoning” from “direct answering”.</td>
+      <td>
+        <em>Dual-regime data</em><br>
+        • <strong>Think-off</strong> queries labeled via a custom tagging system.<br>
+        • <strong>Think-on</strong> queries generated by a multi-agent solver.<br><br>
+        <em>Knowledge Distillation&nbsp;+&nbsp;Multi-Token Prediction</em> for fine-grained utility.
+      </td>
+      <td>Base model attains strong factual and reasoning skills without full-scale pre-training costs.</td>
+    </tr>
+    <tr>
+      <td><strong>2. Post-training</strong></td>
+      <td>Make reasoning optional and efficient.</td>
+      <td>
+        <em>Cold-start AutoThink</em> — majority vote sets the initial thinking mode.<br>
+        <em>Step-SRPO</em> — intermediate supervision rewards correct <strong>mode selection</strong> and <strong>answer accuracy</strong> under that mode.
+      </td>
+      <td>Model triggers CoT only when beneficial, reducing token use and speeding inference.</td>
+    </tr>
+  </tbody>
+</table>
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/61ee40a269351366e29972ad/cwFAEh7Rl3f4FU46z8gBZ.png)
+
+
+***
+
+# Data Format
+
+
+KAT produces responses in a **structured template** that makes the reasoning path explicit and machine-parsable.  
+Two modes are supported:
+
+
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61ee40a269351366e29972ad/H8iAvQMMT02nyvlYnI5q1.jpeg)
+
+
+## Special Tokens
+
+| Token | Description |
+|-------|-------------|
+| `<judge>` | Analyzes the input to decide whether explicit reasoning is needed. |
+| `<think_on>` / `<think_off>` | Indicates whether reasoning is **activated** (“on”) or **skipped** (“off”). |
+| `<think>` | Marks the start of the chain-of-thought segment when `think_on` is chosen. |
+| `<answer>` | Marks the start of the final user-facing answer. |
+
+
+***
+
+# 🔧 Quick Start
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "Kwaipilot/KAT-V1-40B"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+# prepare the model input
+prompt = "Give me a short introduction to large language model."
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=65536,
+    temperature=0.6,
+    top_p=0.95,
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 
+content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")
+print("prompt:\n", prompt)
+print("content:\n", content)
+"""
+prompt:
+Give me a short introduction to large language model.
+content:
+<judge>
+The user's request is to provide a concise factual introduction to large language models, which involves retrieving and summarizing basic information. This task is straightforward as it only requires recalling and presenting well-known details without deeper analysis. No complex reasoning is needed here—just a simple explanation will suffice.
+</judge>
+
+<think_off>
+<answer>
+A **Large Language Model (LLM)** is an advanced AI system trained on vast amounts of text data to understand, generate, and process human-like language. Here’s a concise introduction:
+
+### Key Points:
+1. **Training**: Trained on diverse text sources (books, websites, etc.) using deep learning.
+2. **Capabilities**: 
+   - Answer questions, generate text, summarize content, translate languages.
+   - Understand context, sentiment, and nuances in language.
+3. **Architecture**: Often based on **transformer models** (e.g., BERT, GPT, LLaMA).
+4. **Scale**: Billions of parameters, requiring massive computational resources.
+5. **Applications**: Chatbots, content creation, coding assistance, research, and more.
+
+### Examples:
+- **OpenAI’s GPT-4**: Powers ChatGPT.
+- **Google’s Gemini**: Used in Bard.
+- **Meta’s LLaMA**: Open-source alternative.
+
+### Challenges:
+- **Bias**: Can reflect biases in training data.
+- **Accuracy**: May hallucinate "facts" not grounded in reality.
+- **Ethics**: Raises concerns about misinformation and job displacement.
+
+LLMs represent a leap forward in natural language processing, enabling machines to interact with humans in increasingly sophisticated ways. 🌐🤖
+</answer>
+"""
+```
+
+***
+
+# Future Releases
+
+Looking ahead, we will publish a companion paper that fully documents the **AutoThink training framework**, covering:
+
+* Cold-start initialization procedures  
+* Reinforcement-learning (Step-SRPO) strategies  
+* Data curation and reward design details  
+
+At the same time, we will open-source:
+
+* **Training resources** – the curated dual-regime datasets and RL codebase  
+* **Model suite** – checkpoints at 1.5B, 7B, and 13B parameters, all trained with AutoThink gating  

added_tokens.json

@@ -0,0 +1,32 @@
+{
+  "</answer>": 151670,
+  "</judge>": 151668,
+  "</think>": 151666,
+  "</tool_call>": 151658,
+  "<answer>": 151669,
+  "<judge>": 151667,
+  "<think>": 151665,
+  "<think_off>": 151672,
+  "<think_on>": 151671,
+  "<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
+}

chat_template.jinja

@@ -0,0 +1,88 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- '' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\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" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" and not message.tool_calls %}
+        {%- set content = message.content %}
+        {%- if not loop.last %}
+            {%- set answer_blocks = message.content.split('<answer>\n') %}
+            {%- if answer_blocks|length > 1 %}
+                {%- set last_answer_block = answer_blocks[-1] %}
+                {%- if '\n</answer>' in last_answer_block %}
+                    {%- set content = last_answer_block.split('\n</answer>')[0] %}
+                {%- else %}
+                    {%- set content = message.content.split('<think_off>')[-1].lstrip('\n') %}
+                    {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+                {%- endif %}
+            {%- else %}
+                {%- set content = message.content.split('<think_off>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- if not loop.last %}
+            {%- set answer_blocks = message.content.split('<answer>\n') %}
+            {%- if answer_blocks|length > 1 %}
+                {%- set last_answer_block = answer_blocks[-1] %}
+                {%- if '\n</answer>' in last_answer_block %}
+                    {%- set content = last_answer_block.split('\n</answer>')[0] %}
+                {%- else %}
+                    {%- set content = message.content.split('<think_off>')[-1].lstrip('\n') %}
+                    {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+                {%- endif %}
+            {%- else %}
+                {%- set content = message.content.split('<think_off>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{\"name\": \"' }}
+            {{- tool_call.name }}
+            {{- '\", \"arguments\": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

@@ -0,0 +1,36 @@
+{
+  "name_or_path": "tclf90/KAT-V1-40B-AWQ",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 27648,
+  "max_position_embeddings": 131072,
+  "max_window_layers": 64,
+  "model_type": "qwen2",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 80,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.46.1",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 152064,
+  "quantization_config": {
+    "quant_method": "awq",
+    "bits": 4,
+    "group_size": 128,
+    "version": "gemm",
+    "zero_point": true
+  }
+}

configuration.json

@@ -0,0 +1 @@
+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

@@ -0,0 +1,12 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_p": 0.95,
+  "transformers_version": "4.52.4"
+}

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+size 4990397496

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+size 4972882976

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+size 4998781176

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+size 3458780720

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:528d9f2690ed3679eee70ed9b085fb78899b7f6dfc2a220220cfe89fdd3ffef5
+size 11423388

tokenizer_config.json

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+{
+  "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
+    },
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+      "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
+    },
+    "151667": {
+      "content": "<judge>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151668": {
+      "content": "</judge>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151669": {
+      "content": "<answer>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151670": {
+      "content": "</answer>",
+      "lstrip": false,
+      "normalized": false,
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+      "single_word": false,
+      "special": false
+    },
+    "151671": {
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151672": {
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+      "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,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "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
+}