Instructions to use animeshkundu/cmd-correct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use animeshkundu/cmd-correct with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("animeshkundu/cmd-correct", dtype="auto") - llama-cpp-python
How to use animeshkundu/cmd-correct with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="animeshkundu/cmd-correct", filename="qwen3-correct-0.6B.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 animeshkundu/cmd-correct with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf animeshkundu/cmd-correct # Run inference directly in the terminal: llama-cli -hf animeshkundu/cmd-correct
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf animeshkundu/cmd-correct # Run inference directly in the terminal: llama-cli -hf animeshkundu/cmd-correct
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 animeshkundu/cmd-correct # Run inference directly in the terminal: ./llama-cli -hf animeshkundu/cmd-correct
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 animeshkundu/cmd-correct # Run inference directly in the terminal: ./build/bin/llama-cli -hf animeshkundu/cmd-correct
Use Docker
docker model run hf.co/animeshkundu/cmd-correct
- LM Studio
- Jan
- Ollama
How to use animeshkundu/cmd-correct with Ollama:
ollama run hf.co/animeshkundu/cmd-correct
- Unsloth Studio new
How to use animeshkundu/cmd-correct 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 animeshkundu/cmd-correct 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 animeshkundu/cmd-correct to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for animeshkundu/cmd-correct to start chatting
- Pi new
How to use animeshkundu/cmd-correct with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf animeshkundu/cmd-correct
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": "animeshkundu/cmd-correct" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use animeshkundu/cmd-correct with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf animeshkundu/cmd-correct
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 animeshkundu/cmd-correct
Run Hermes
hermes
- Docker Model Runner
How to use animeshkundu/cmd-correct with Docker Model Runner:
docker model run hf.co/animeshkundu/cmd-correct
- Lemonade
How to use animeshkundu/cmd-correct with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull animeshkundu/cmd-correct
Run and chat with the model
lemonade run user.cmd-correct-{{QUANT_TAG}}List all available models
lemonade list
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf animeshkundu/cmd-correct# Run inference directly in the terminal:
llama-cli -hf animeshkundu/cmd-correctUse 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 animeshkundu/cmd-correct# Run inference directly in the terminal:
./llama-cli -hf animeshkundu/cmd-correctBuild 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 animeshkundu/cmd-correct# Run inference directly in the terminal:
./build/bin/llama-cli -hf animeshkundu/cmd-correctUse Docker
docker model run hf.co/animeshkundu/cmd-correctcmd-correct
Shell command correction models for the fix CLI. Fine-tuned Qwen3 models that correct typos, fix case errors, and translate natural language to shell commands.
Available Models
| Model | Size | Base | Description |
|---|---|---|---|
| qwen3-correct-0.6B.gguf | 378 MB | Qwen3-0.6B | Fast, lightweight (default) |
| qwen3-correct-1.7B.gguf | ~1 GB | Qwen3-1.7B | Higher quality, more capable |
Quick Start
Install the CLI
macOS (Homebrew):
brew tap animeshkundu/fix
brew install fix
From Source (Rust):
cargo install --git https://github.com/animeshkundu/fix --features metal # macOS
cargo install --git https://github.com/animeshkundu/fix --features cuda # Linux/Windows with NVIDIA
cargo install --git https://github.com/animeshkundu/fix # CPU only
Usage
# Basic correction
fix gti status
# Output: git status
# With shell override
fix --shell powershell Get-ChidItem
# Output: Get-ChildItem
# Switch to larger model
fix --use-model qwen3-correct-1.7B
# List available models
fix --list-models
Supported Shells
bash, zsh, fish, powershell, cmd, tcsh
Model Details
Training
- Method: LoRA fine-tuning with MLX on Apple Silicon
- Dataset: 135K synthetic examples (typos, case errors, natural language)
- Format: ChatML with
<|im_start|>/<|im_end|>tokens - Quantization: Q4_K_M with importance matrix
Prompt Template
<|im_start|>system
You are a shell command corrector for {shell}. Output only the corrected command.<|im_end|>
<|im_start|>user
{incorrect_command}<|im_end|>
<|im_start|>assistant
Example Corrections
| Input | Output | Type |
|---|---|---|
| gti status | git status | typo |
| git remote -V | git remote -v | case |
| dcokre ps | docker ps | typo |
| I need to list docker images | docker images | natural language |
Links
- CLI: github.com/animeshkundu/fix
- Website: animeshkundu.github.io/fix
License
Apache 2.0
- Downloads last month
- 4
We're not able to determine the quantization variants.
Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf animeshkundu/cmd-correct# Run inference directly in the terminal: llama-cli -hf animeshkundu/cmd-correct