Upload MODEL_CARD.md

---

license: mit
data:

* Chinese conversational pairs (Weibo, Zhihu)
* English conversational pairs (Reddit, StackExchange)
* Domain-specific Q\&A (IT, healthcare, finance)
language:
* zh
* en
metrics:
* C-Eval EM: 68.3%
* GPT4Bot-Bench F1: 72.1%
* 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: see Evaluation Results section
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`

## Model Details

* **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)

## Model Description

`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

* 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.

## 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.

## Citation

```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}}
}
```

MODEL_CARD.md

@@ -1,36 +1,129 @@
 ---
-license: mit
 
+license: mit
 data:
 
-Chinese conversational pairs (Weibo, Zhihu)
+* Chinese conversational pairs (Weibo, Zhihu)
+* English conversational pairs (Reddit, StackExchange)
+* Domain-specific Q\&A (IT, healthcare, finance)
+  language:
+* zh
+* en
+  metrics:
+* C-Eval EM: 68.3%
+* GPT4Bot-Bench F1: 72.1%
+* 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: see Evaluation Results section
+  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`
+
+## Model Details
+
+* **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)
+
+## Model Description
+
+`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**
 
-English conversational pairs (Reddit, StackExchange)
+   ```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:
 
-Domain-specific Q&A (IT, healthcare, finance)
+   ```bash
+   python convert-llama2-to-gguf.py /path/to/llama2-7b /models/llama2-7b.gguf
+   ```
+3. **Run Inference API:**
 
-language: zh, en
+   ```bash
+   uvicorn app:app --host 0.0.0.0 --port 8000 --reload
+   ```
+4. **Sample Request:**
 
-metrics:
+   ```bash
+   curl -X POST http://localhost:8000/generate \
+     -H "Content-Type: application/json" \
+     -d '{"prompt": "你好，世界！", "token": "YOUR_SECURE_TOKEN"}'
+   ```
 
-C-Eval EM: 68.3%
+## Training Data
 
-GPT4Bot-Bench F1: 72.1%
+* **Base Model:** LLaMA 2 7B (Meta)
+* **Fine-Tuning Data:**
 
-SelfChat Sim: 0.87
+  * 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
 
-base_model: LLaMA 2 7B
+## Evaluation Results
 
-new_version: 1.0.0
+| 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            |
 
-pipeline_tag: text-generation, conversational
+## Limitations
 
-auto_detect: language, sentiment
+* 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.
 
-library_name: llama.cpp, FastAPI
+## Ethical Considerations
 
-tags: chatbot, self-hosted, bilingual, low-latency
+* **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.
 
-eval_results: see Evaluation Results section
+## Citation
 
-documentation: https://huggingface.co/your-username/my-chatbot-llama2-7b
+```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}}
+}
+```