|
|
--- |
|
|
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). |
|
|
|
