Instructions to use DominuZ/jq-coder-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

DominuZ commited on 1 day ago

Commit

627e0a2

verified ·

1 Parent(s): 2f28647

Card: jqc 0.2.0 (cargo install, --write com confirmacao, sessao interativa)

Browse files

Files changed (1) hide show

README.md +8 -3

README.md CHANGED Viewed

@@ -99,14 +99,19 @@ straight from this repo also works: `ollama run hf.co/DominuZ/jq-coder-0.6B:Q8_0
 ## Or the `jqc` CLI — one binary, batteries included
-Prebuilt binaries for Windows, Linux and macOS (Apple Silicon) on the
-[Releases page](https://github.com/EdelmarSchneider/jq-coder-cli/releases). It embeds
-llama.cpp and runs the generated filter for you; the model downloads on first use.
 ```bash
 jqc "get the id of every order" orders.json
 ```
 ## Or llama.cpp / LM Studio
 ```bash

 ## Or the `jqc` CLI — one binary, batteries included
+`cargo install jqc`, or prebuilt binaries for Windows, Linux and macOS (Apple Silicon)
+on the [Releases page](https://github.com/EdelmarSchneider/jq-coder-cli/releases). It
+embeds llama.cpp and runs the generated filter for you; the model downloads on first use.
 ```bash
 jqc "get the id of every order" orders.json
 ```
+Since v0.2.0 it can also **write the result back into the file** (`--write`: shows a
+diff, asks first, keeps a `.bak`, writes atomically) and offers an **interactive
+session** (`jqc orders.json`): chain requests against a working buffer with apply/undo,
+and the file on disk only changes when you confirm `:w`.
 ## Or llama.cpp / LM Studio
 ```bash