Instructions to use nawazadroit/vaidya1st_toolcalling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nawazadroit/vaidya1st_toolcalling with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nawazadroit/vaidya1st_toolcalling", dtype="auto") - llama-cpp-python
How to use nawazadroit/vaidya1st_toolcalling with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="nawazadroit/vaidya1st_toolcalling", filename="unsloth.Q8_0.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use nawazadroit/vaidya1st_toolcalling with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf nawazadroit/vaidya1st_toolcalling:Q8_0 # Run inference directly in the terminal: llama cli -hf nawazadroit/vaidya1st_toolcalling:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf nawazadroit/vaidya1st_toolcalling:Q8_0 # Run inference directly in the terminal: llama cli -hf nawazadroit/vaidya1st_toolcalling:Q8_0
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf nawazadroit/vaidya1st_toolcalling:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf nawazadroit/vaidya1st_toolcalling:Q8_0
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf nawazadroit/vaidya1st_toolcalling:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf nawazadroit/vaidya1st_toolcalling:Q8_0
Use Docker
docker model run hf.co/nawazadroit/vaidya1st_toolcalling:Q8_0
- LM Studio
- Jan
- Ollama
How to use nawazadroit/vaidya1st_toolcalling with Ollama:
ollama run hf.co/nawazadroit/vaidya1st_toolcalling:Q8_0
- Unsloth Studio
How to use nawazadroit/vaidya1st_toolcalling with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for nawazadroit/vaidya1st_toolcalling to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for nawazadroit/vaidya1st_toolcalling to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for nawazadroit/vaidya1st_toolcalling to start chatting
- Pi
How to use nawazadroit/vaidya1st_toolcalling with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf nawazadroit/vaidya1st_toolcalling:Q8_0
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "nawazadroit/vaidya1st_toolcalling:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use nawazadroit/vaidya1st_toolcalling with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf nawazadroit/vaidya1st_toolcalling:Q8_0
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default nawazadroit/vaidya1st_toolcalling:Q8_0
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use nawazadroit/vaidya1st_toolcalling with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf nawazadroit/vaidya1st_toolcalling:Q8_0
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "nawazadroit/vaidya1st_toolcalling:Q8_0" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use nawazadroit/vaidya1st_toolcalling with Docker Model Runner:
docker model run hf.co/nawazadroit/vaidya1st_toolcalling:Q8_0
- Lemonade
How to use nawazadroit/vaidya1st_toolcalling with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull nawazadroit/vaidya1st_toolcalling:Q8_0
Run and chat with the model
lemonade run user.vaidya1st_toolcalling-Q8_0
List all available models
lemonade list
llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)π§ Llama 3.2 - 1B Instruct | Toolcalling Test Finetuned Model (ADROIT NOT USING ANYMORE DEVELOPED FOR TESTING ONLY)
- Developed by: nawazadroit
- License: Apache-2.0
- Finetuned from base model:
unsloth/Llama-3.2-1B-Instruct
π§ͺ Purpose
This is a simple tool-calling test model, finetuned specifically on a custom dataset (dataset.json) related to scheme application workflows. It is designed for structured assistant-like behavior with basic tool invocation capabilities, especially in the domain of public healthcare scheme assistance (e.g., applying to MJPJAY / PM-JAY schemes in government hospitals).
π Features
- β Lightweight 1B parameter model
- β Uses Huggingface's TRL library for reward modeling and instruction tuning
- β Ideal for testing local tool-calling setups
- β Compatible with GGUF format for efficient inference
π Dataset Info
The model was trained on a JSON dataset (dataset.json) containing multi-turn dialogues structured for:
- Verifying patient eligibility
- Applying health schemes
- Extracting document information
- Calling specific tools via structured API-like responses
π Links
- π Hugging Face Transformers
- π¬ TRL Library
- Downloads last month
- 6
8-bit
Model tree for nawazadroit/vaidya1st_toolcalling
Base model
meta-llama/Llama-3.2-1B-Instruct
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="nawazadroit/vaidya1st_toolcalling", filename="unsloth.Q8_0.gguf", )