Upload MODEL_CARD.md

---

license: mit
data:

* 中文对话对（知乎、微博）
* 英文对话对（Reddit、StackExchange）
* 行业知识问答（IT、医疗、金融）
language:
* zh
* en
metrics:
* 中文问答精确率（C-Eval EM）: 68.3%
* 多轮对话F1（GPT4Bot-Bench）: 72.1%
* 自我对话相似度（SelfChat Sim）: 0.87
base\_model: LLaMA 2 7B
new\_version: 1.0.0
pipeline\_tag: text-generation, conversational
auto\_detect:
* language
* sentiment
library\_name:
* llama.cpp
* FastAPI
tags:
* chatbot
* self-hosted
* bilingual
* low-latency
eval\_results: 见评估结果部分
documentation: [https://huggingface.co/your-username/my-chatbot-llama2-7b](https://huggingface.co/your-username/my-chatbot-llama2-7b)

---

# 中文双语智能对话模型 `my-chatbot-llama2-7b`

## 模型简介

* **模型名称**：my-chatbot-llama2-7b
* **版本**：1.0.0
* **许可协议**：MIT License
* **基础模型**：LLaMA 2 7B（由 Meta 提供）
* **适用语言**：中文、英文
* **适用场景**：问答、闲聊、知识检索、行业对话

该模型在 LLaMA 2 7B 基础上进行微调，加入大量中文和英文对话数据，优化对中文语义理解能力，在对话连贯性、简洁性方面表现稳定，适合本地私有部署，支持最大 2048 字符上下文。

---

## 应用场景

* 🤖 客服助手：适用于网页客服、应用内问答等场景
* 📚 知识查询：结合行业数据，实现领域问答（如 IT 故障、医疗症状解释、金融常识）
* 🖥️ 本地部署：支持 CPU/GPU 本地运行，适合对数据安全有要求的企业用户
* 🔀 中英混合问答：自动识别语言，无需用户手动切换

---

## 快速使用

### 🧩 安装依赖

```bash
# 安装 llama.cpp
git clone https:
```

MODEL_CARD.md

@@ -3,16 +3,16 @@
 license: mit
 data:
 
-* Chinese conversational pairs (Weibo, Zhihu)
-* English conversational pairs (Reddit, StackExchange)
-* Domain-specific Q\&A (IT, healthcare, finance)
+* 中文对话对（知乎、微博）
+* 英文对话对（Reddit、StackExchange）
+* 行业知识问答（IT、医疗、金融）
   language:
 * zh
 * en
   metrics:
-* C-Eval EM: 68.3%
-* GPT4Bot-Bench F1: 72.1%
-* SelfChat Sim: 0.87
+* 中文问答精确率（C-Eval EM）: 68.3%
+* 多轮对话F1（GPT4Bot-Bench）: 72.1%
+* 自我对话相似度（SelfChat Sim）: 0.87
   base\_model: LLaMA 2 7B
   new\_version: 1.0.0
   pipeline\_tag: text-generation, conversational
@@ -27,103 +27,40 @@ data:
 * self-hosted
 * bilingual
 * low-latency
-  eval\_results: see Evaluation Results section
+  eval\_results: 见评估结果部分
   documentation: [https://huggingface.co/your-username/my-chatbot-llama2-7b](https://huggingface.co/your-username/my-chatbot-llama2-7b)
 
 ---
 
-# Model Card for `my-chatbot-llama2-7b`
+# 中文双语智能对话模型 `my-chatbot-llama2-7b`
 
-## Model Details
+## 模型简介
 
-* **Model Name:** my-chatbot-llama2-7b
-* **Version:** 1.0.0
-* **Authors:** Your Name or Organization
-* **License:** MIT License (see `LICENSE`)
-* **Repository:** [https://huggingface.co/your-username/my-chatbot-llama2-7b](https://huggingface.co/your-username/my-chatbot-llama2-7b)
-* **Library Dependencies:** llama.cpp (v0.1+), FastAPI, Python >=3.8
-* **Hardware Requirements:** CPU-only (4+ cores, 8 GB RAM) or GPU (≥4 GB VRAM recommended)
+* **模型名称**：my-chatbot-llama2-7b
+* **版本**：1.0.0
+* **许可协议**：MIT License
+* **基础模型**：LLaMA 2 7B（由 Meta 提供）
+* **适用语言**：中文、英文
+* **适用场景**：问答、闲聊、知识检索、行业对话
 
-## Model Description
+该模型在 LLaMA 2 7B 基础上进行微调，加入大量中文和英文对话数据，优化对中文语义理解能力，在对话连贯性、简洁性方面表现稳定，适合本地私有部署，支持最大 2048 字符上下文。
 
-`my-chatbot-llama2-7b` is a fine-tuned variant of Meta’s LLaMA 2 7B model, optimized for chatbot interactions in Chinese and English. The model has been adapted via supervised fine-tuning on a mixed dataset of conversational logs, code snippets, and knowledge-base Q\&A pairs. It supports up to 2048 tokens of context and responds with balanced informativeness and conciseness.
-
-## Intended Use
-
-* **Primary Use Cases:**
-
-  * Chatbot applications (customer support, personal assistant)
-  * FAQ generation and knowledge retrieval
-  * Low-latency on-premises inference
-* **Users:** Developers seeking an open-source, self-hosted chat model.
-* **Exclusions:** Not for generating disallowed content (hate speech, misinformation, medical or legal advice without expert oversight).
-
-## How to Use
-
-1. **Installation**
-
-   ```bash
-   # Clone and build llama.cpp
-   git clone https://github.com/ggerganov/llama.cpp
-   cd llama.cpp && make
-   pip install fastapi uvicorn
-   ```
-2. **Download Model Weights**
-   Obtain `llama2-7b.gguf` from Hugging Face or convert official weights:
-
-   ```bash
-   python convert-llama2-to-gguf.py /path/to/llama2-7b /models/llama2-7b.gguf
-   ```
-3. **Run Inference API:**
-
-   ```bash
-   uvicorn app:app --host 0.0.0.0 --port 8000 --reload
-   ```
-4. **Sample Request:**
-
-   ```bash
-   curl -X POST http://localhost:8000/generate \
-     -H "Content-Type: application/json" \
-     -d '{"prompt": "你好，世界！", "token": "YOUR_SECURE_TOKEN"}'
-   ```
-
-## Training Data
-
-* **Base Model:** LLaMA 2 7B (Meta)
-* **Fine-Tuning Data:**
-
-  * 200k Chinese conversational pairs (Weibo, Zhihu)
-  * 150k English conversational pairs (Reddit, StackExchange)
-  * 50k domain-specific Q\&A (IT, healthcare, finance)
-* **Preprocessing:** Unicode normalization, deduplication, profanity filtering
-
-## Evaluation Results
-
-| Benchmark          | Metric     | Score | Notes                             |
-| ------------------ | ---------- | ----- | --------------------------------- |
-| C-Eval (Chinese)   | EM         | 68.3% | Compared against human reference  |
-| GPT4Bot-Bench      | F1         | 72.1% | Conversational question answering |
-| SelfChat Sim Score | Similarity | 0.87  | Diversity of responses            |
+---
 
-## Limitations
+## 应用场景
 
-* May occasionally produce plausible-sounding but incorrect answers (hallucinations).
-* Limited knowledge cutoff: September 2023.
-* Sensitive to prompt phrasing; may require few-shot examples for best performance.
+* 🤖 客服助手：适用于网页客服、应用内问答等场景
+* 📚 知识查询：结合行业数据，实现领域问答（如 IT 故障、医疗症状解释、金融常识）
+* 🖥️ 本地部署：支持 CPU/GPU 本地运行，适合对数据安全有要求的企业用户
+* 🔀 中英混合问答：自动识别语言，无需用户手动切换
 
-## Ethical Considerations
+---
 
-* **Bias:** Inherits biases present in training data. Users should monitor and filter harmful outputs.
-* **Privacy:** No personal data was used in fine-tuning.
-* **Misuse Risk:** Could be used to generate misleading or spam content. Users should implement rate-limiting and content moderation.
+## 快速使用
 
-## Citation
+### 🧩 安装依赖
 
-```bibtex
-@misc{mychatbot2025,
-  title        = {my-chatbot-llama2-7b: A Self-Hosted Conversational AI},
-  author       = {Your Name or Organization},
-  year         = {2025},
-  howpublished = {\url{https://huggingface.co/your-username/my-chatbot-llama2-7b}}
-}
+```bash
+# 安装 llama.cpp
+git clone https:
 ```