Instructions to use jnjj/Gvv with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jnjj/Gvv with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jnjj/Gvv", dtype="auto") - llama-cpp-python
How to use jnjj/Gvv with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="jnjj/Gvv", filename="Qwen3-0.6B.i1-IQ1_S.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use jnjj/Gvv with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf jnjj/Gvv:IQ1_S # Run inference directly in the terminal: llama-cli -hf jnjj/Gvv:IQ1_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf jnjj/Gvv:IQ1_S # Run inference directly in the terminal: llama-cli -hf jnjj/Gvv:IQ1_S
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 jnjj/Gvv:IQ1_S # Run inference directly in the terminal: ./llama-cli -hf jnjj/Gvv:IQ1_S
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 jnjj/Gvv:IQ1_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf jnjj/Gvv:IQ1_S
Use Docker
docker model run hf.co/jnjj/Gvv:IQ1_S
- LM Studio
- Jan
- Ollama
How to use jnjj/Gvv with Ollama:
ollama run hf.co/jnjj/Gvv:IQ1_S
- Unsloth Studio new
How to use jnjj/Gvv 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 jnjj/Gvv 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 jnjj/Gvv to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for jnjj/Gvv to start chatting
- Pi new
How to use jnjj/Gvv with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf jnjj/Gvv:IQ1_S
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": "jnjj/Gvv:IQ1_S" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use jnjj/Gvv with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf jnjj/Gvv:IQ1_S
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 jnjj/Gvv:IQ1_S
Run Hermes
hermes
- Docker Model Runner
How to use jnjj/Gvv with Docker Model Runner:
docker model run hf.co/jnjj/Gvv:IQ1_S
- Lemonade
How to use jnjj/Gvv with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull jnjj/Gvv:IQ1_S
Run and chat with the model
lemonade run user.Gvv-IQ1_S
List all available models
lemonade list
jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF
This model was converted to GGUF format from jnjj/model_no_bias_qwen3-0.6B using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF --hf-file model_no_bias_qwen3-0.6b-q3_k_l.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF --hf-file model_no_bias_qwen3-0.6b-q3_k_l.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF --hf-file model_no_bias_qwen3-0.6b-q3_k_l.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF --hf-file model_no_bias_qwen3-0.6b-q3_k_l.gguf -c 2048
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jnjj/model_no_bias_qwen3-0.6B