---
license: mit
tags:
  - conversational
  - text-generation
  - instruction-tuned
  - chat
  - dialogue
language:
  - en
datasets:
  - yashsoni78/conversation_data_mcp_100
library_name: transformers
pipeline_tag: text-generation
---

# 🛠️ MCP Tool Model

The **MCP Tool Model** is an instruction-tuned conversational language model fine-tuned on the [`conversation_data_mcp_100`](https://huggingface.co/datasets/yashsoni78/conversation_data_mcp_100) dataset. Built to handle multi-turn dialogues with clarity and coherence, this model is ideal for chatbot development, virtual assistants, or any conversational AI tasks.

## 🧠 Model Details

- **Base Model**: *mistralai/Mistral-7B-Instruct-v0.2*
- **Fine-tuned on**: Custom multi-turn conversation dataset (`yashsoni78/conversation_data_mcp_100`)
- **Languages**: English
- **Use case**: General-purpose chatbot or instruction-following agent

## 🚀 Example Usage

You can load and use the model with the Hugging Face Transformers library:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "yashsoni78/mcp_tool_model"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "User: How do I reset my password?\nAssistant:"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

> 💡 Make sure to adapt the prompt formatting depending on your training setup (e.g., special tokens, roles, etc.)

## 📚 Training Data

This model was fine-tuned on the [MCP 100 conversation dataset](https://huggingface.co/datasets/yashsoni78/conversation_data_mcp_100), consisting of 100 high-quality multi-turn dialogues between users and assistants. Each exchange is structured to reflect real-world inquiry-response flows.

## 📊 Intended Use

- Chatbots for websites or tools
- Instruction-following agents
- Dialogue research
- Voice assistant backend

## ⚠️ Limitations

- May hallucinate facts or generate inaccurate responses.
- Trained on a small dataset (100 dialogues), so generalization may be limited.
- English only.

## 📜 License

This model is licensed under the [MIT License](./LICENSE). You are free to use, modify, and distribute it with attribution.

## 🙏 Acknowledgements

Special thanks to the open-source community and Hugging Face for providing powerful tools to build and share models easily.

## 📬 Contact

For issues, feedback, or collaborations, feel free to reach out to [@yashsoni78](https://huggingface.co/yashsoni78).