初始化项目，由ModelHub XC社区提供模型

Model: tclf90/qwq-32b-gptq-int8
Source: Original Platform

README.md

@@ -0,0 +1,97 @@
+---
+license: qwen
+license_name: Tongyi Qianwen LICENSE AGREEMENT
+license_link: LICENSE
+pipeline_tag: text-generation
+tags:
+- QwQ
+- gptq
+- int8
+- 量化修复
+- vLLM
+- sglang
+base_model:
+  - Qwen/QwQ-32B
+base_model_relation: quantized
+---
+# 通义千问 QwQ-32B-GPTQ-Int8
+基础型 [Qwen/QwQ-32B](https://www.modelscope.cn/models/Qwen/QwQ-32B)
+
+### 【模型更新日期】
+``` 
+2025-04-24
+1. add group 128v3
+2. 用黑科技修掉之前 Compute 7 显卡（GPTQ格式）运行时候出现的感叹号问题
+3. 删除对话模板的<think>字符，使得模型作答的时候，能完整吐出<think>模块
+4. 提供一个无思考模式的对话模板（tokenizer_config-无思考模式.json）供参考把玩。
+
+2025-03-07
+1. add group 128
+```
+
+### 【依赖】
+
+```
+vllm==0.8.4
+transformers==4.51.3
+```
+
+<div style="
+    background: rgba(255, 193, 61, 0.15);
+    padding: 16px;
+    border-radius: 6px;
+    border: 1px solid rgba(255, 165, 0, 0.3);
+    margin: 16px 0;
+">
+### 【💡新版 VLLM 注意事项💡】
+
+#### 1. 需使用V0推理模式
+启动vllm之前，先设置环境变量
+```
+export VLLM_USE_V1=0
+```
+</div>
+
+### 【模型列表】
+
+| tag      | 文件大小   | 最近更新时间       |
+|----------|--------|--------------|
+| `g128v3` | `33GB` | `2025-04-24` |
+| `g128`   | `33GB` | `2025-03-07` |
+
+
+### 【模型下载】
+
+```python
+from modelscope import snapshot_download
+snapshot_download('tclf90/qwq-32b-gptq-int8', cache_dir="本地路径", revision='g128v3')
+```
+
+### 【修复内容】
+
+1. 对GPTQ量化的校准做了额外优化；减少模型的 `1.乱吐字`、`2.无限循环`、`3.长文能力丢失`等情况。
+2. 支持1卡、2卡及4卡的`tensor-parallel-size`启动。
+
+
+### 【介绍】
+<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
+    <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
+</a>
+
+## Introduction
+
+QwQ is the reasoning model of the Qwen series. Compared with conventional instruction-tuned models, QwQ, which is capable of thinking and reasoning, can achieve significantly enhanced performance in downstream tasks, especially hard problems. QwQ-32B is the medium-sized reasoning model, which is capable of achieving competitive performance against state-of-the-art reasoning models, e.g., DeepSeek-R1, o1-mini.
+
+**This repo contains the QwQ 32B model**, which has the following features:
+- Type: Causal Language Models
+- Training Stage: Pretraining & Post-training (Supervised Finetuning and Reinforcement Learning)
+- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
+- Number of Parameters: 32.5B
+- Number of Paramaters (Non-Embedding): 31.0B
+- Number of Layers: 64
+- Number of Attention Heads (GQA): 40 for Q and 8 for KV
+- Context Length: Full 131,072 tokens
+
+**Note:** For the best experience, please review the [usage guidelines](#usage-guidelines) before deploying QwQ models.
+
+For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/).