Instructions to use yugsisodiya/jarvis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use yugsisodiya/jarvis 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 yugsisodiya/jarvis # Run inference directly in the terminal: llama cli -hf yugsisodiya/jarvis
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf yugsisodiya/jarvis # Run inference directly in the terminal: llama cli -hf yugsisodiya/jarvis
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 yugsisodiya/jarvis # Run inference directly in the terminal: ./llama-cli -hf yugsisodiya/jarvis
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 yugsisodiya/jarvis # Run inference directly in the terminal: ./build/bin/llama-cli -hf yugsisodiya/jarvis
Use Docker
docker model run hf.co/yugsisodiya/jarvis
- LM Studio
- Jan
- vLLM
How to use yugsisodiya/jarvis with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "yugsisodiya/jarvis" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "yugsisodiya/jarvis", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/yugsisodiya/jarvis
- Ollama
How to use yugsisodiya/jarvis with Ollama:
ollama run hf.co/yugsisodiya/jarvis
- Unsloth Desktop
- Docker Model Runner
How to use yugsisodiya/jarvis with Docker Model Runner:
docker model run hf.co/yugsisodiya/jarvis
- Lemonade
How to use yugsisodiya/jarvis with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull yugsisodiya/jarvis
Run and chat with the model
lemonade run user.jarvis-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Jarvis (v2)
Jarvis is an advanced neural intelligence model trained and compiled with the J.A.R.V.I.S. Neural Forge studio by Yug Sisodiya.
🚀 Quickstart with Ollama
Run this model with a single command via Ollama:
ollama run hf.co/yugsisodiya/jarvis
Model Overview
- Model Name: Jarvis
- Release Version: v2
- Base Foundation: Jarvis (J.A.R.V.I.S. Neural Architecture)
- Neural Architecture: jarvis-grand-architecture
- Context Window: 131072 tokens
- GGUF Weights:
Available (jarvis.gguf)
Architecture Highlights
- Fully integrated with the J.A.R.V.I.S. unified cognitive assistant architecture.
- Optimized for low-latency interactive inference, multi-domain reasoning, and executive problem solving.
- Checkpointed neural weights verified in local studio runtime.
Platform Integration
This model is accessible within the J.A.R.V.I.S. Command Console on port 4700 and compatible with local neural inference pipelines.
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Hardware compatibility
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