Text Generation
GGUF
English
Portuguese
jq
json
text-to-jq
natural-language-to-code
code-generation
text-to-code
qwen3
ollama
llama.cpp
offline
privacy
structured-data
portuguese
Eval Results (legacy)
conversational
Instructions to use DominuZ/jq-coder-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use DominuZ/jq-coder-0.6B with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="DominuZ/jq-coder-0.6B", filename="jq-coder-v13-release-Q8_0.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use DominuZ/jq-coder-0.6B 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 DominuZ/jq-coder-0.6B:Q8_0 # Run inference directly in the terminal: llama cli -hf DominuZ/jq-coder-0.6B:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf DominuZ/jq-coder-0.6B:Q8_0 # Run inference directly in the terminal: llama cli -hf DominuZ/jq-coder-0.6B: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 DominuZ/jq-coder-0.6B:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf DominuZ/jq-coder-0.6B: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 DominuZ/jq-coder-0.6B:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf DominuZ/jq-coder-0.6B:Q8_0
Use Docker
docker model run hf.co/DominuZ/jq-coder-0.6B:Q8_0
- LM Studio
- Jan
- vLLM
How to use DominuZ/jq-coder-0.6B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DominuZ/jq-coder-0.6B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DominuZ/jq-coder-0.6B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/DominuZ/jq-coder-0.6B:Q8_0
- Ollama
How to use DominuZ/jq-coder-0.6B with Ollama:
ollama run hf.co/DominuZ/jq-coder-0.6B:Q8_0
- Unsloth Studio
How to use DominuZ/jq-coder-0.6B 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 DominuZ/jq-coder-0.6B 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 DominuZ/jq-coder-0.6B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for DominuZ/jq-coder-0.6B to start chatting
- Atomic Chat new
- Docker Model Runner
How to use DominuZ/jq-coder-0.6B with Docker Model Runner:
docker model run hf.co/DominuZ/jq-coder-0.6B:Q8_0
- Lemonade
How to use DominuZ/jq-coder-0.6B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull DominuZ/jq-coder-0.6B:Q8_0
Run and chat with the model
lemonade run user.jq-coder-0.6B-Q8_0
List all available models
lemonade list
Upload jq-coder-v14-release-f16.gguf with huggingface_hub
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