Instructions to use yashsoni78/mcp_tool_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yashsoni78/mcp_tool_model with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="yashsoni78/mcp_tool_model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("yashsoni78/mcp_tool_model", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use yashsoni78/mcp_tool_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yashsoni78/mcp_tool_model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yashsoni78/mcp_tool_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yashsoni78/mcp_tool_model

SGLang

How to use yashsoni78/mcp_tool_model with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "yashsoni78/mcp_tool_model" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yashsoni78/mcp_tool_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "yashsoni78/mcp_tool_model" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yashsoni78/mcp_tool_model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use yashsoni78/mcp_tool_model with Docker Model Runner:
```
docker model run hf.co/yashsoni78/mcp_tool_model
```

🛠️ MCP Tool Model

The MCP Tool Model is an instruction-tuned conversational language model fine-tuned on the 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:

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, 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. 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.

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