Instructions to use featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF", filename="unsloth-Qwen2.5-Coder-0.5B-Instruct-IQ4_XS.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 featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF 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 featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M
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 featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M
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 featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M
- Ollama
How to use featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF with Ollama:
ollama run hf.co/featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M
- Unsloth Studio
How to use featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF 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 featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF 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 featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF to start chatting
- Pi
How to use featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M
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": "featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M
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 featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M
- Lemonade
How to use featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF-Q4_K_M
List all available models
lemonade list
Upload folder using huggingface_hub
Browse files- .gitattributes +12 -0
- README.md +47 -0
- featherless-quants.png +3 -0
- unsloth-Qwen2.5-Coder-0.5B-Instruct-IQ4_XS.gguf +3 -0
- unsloth-Qwen2.5-Coder-0.5B-Instruct-Q2_K.gguf +3 -0
- unsloth-Qwen2.5-Coder-0.5B-Instruct-Q3_K_L.gguf +3 -0
- unsloth-Qwen2.5-Coder-0.5B-Instruct-Q3_K_M.gguf +3 -0
- unsloth-Qwen2.5-Coder-0.5B-Instruct-Q3_K_S.gguf +3 -0
- unsloth-Qwen2.5-Coder-0.5B-Instruct-Q4_K_M.gguf +3 -0
- unsloth-Qwen2.5-Coder-0.5B-Instruct-Q4_K_S.gguf +3 -0
- unsloth-Qwen2.5-Coder-0.5B-Instruct-Q5_K_M.gguf +3 -0
- unsloth-Qwen2.5-Coder-0.5B-Instruct-Q5_K_S.gguf +3 -0
- unsloth-Qwen2.5-Coder-0.5B-Instruct-Q6_K.gguf +3 -0
- unsloth-Qwen2.5-Coder-0.5B-Instruct-Q8_0.gguf +3 -0
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---
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base_model: unsloth/Qwen2.5-Coder-0.5B-Instruct
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pipeline_tag: text-generation
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quantized_by: featherless-ai-quants
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---
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# unsloth/Qwen2.5-Coder-0.5B-Instruct GGUF Quantizations π
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*Optimized GGUF quantization files for enhanced model performance*
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> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
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---
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## Available Quantizations π
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| Quantization Type | File | Size |
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|-------------------|------|------|
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| IQ4_XS | [unsloth-Qwen2.5-Coder-0.5B-Instruct-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF/blob/main/unsloth-Qwen2.5-Coder-0.5B-Instruct-IQ4_XS.gguf) | 335.16 MB |
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| Q2_K | [unsloth-Qwen2.5-Coder-0.5B-Instruct-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF/blob/main/unsloth-Qwen2.5-Coder-0.5B-Instruct-Q2_K.gguf) | 322.92 MB |
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| Q3_K_L | [unsloth-Qwen2.5-Coder-0.5B-Instruct-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF/blob/main/unsloth-Qwen2.5-Coder-0.5B-Instruct-Q3_K_L.gguf) | 352.25 MB |
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| Q3_K_M | [unsloth-Qwen2.5-Coder-0.5B-Instruct-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF/blob/main/unsloth-Qwen2.5-Coder-0.5B-Instruct-Q3_K_M.gguf) | 339.00 MB |
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| Q3_K_S | [unsloth-Qwen2.5-Coder-0.5B-Instruct-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF/blob/main/unsloth-Qwen2.5-Coder-0.5B-Instruct-Q3_K_S.gguf) | 322.59 MB |
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| Q4_K_M | [unsloth-Qwen2.5-Coder-0.5B-Instruct-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF/blob/main/unsloth-Qwen2.5-Coder-0.5B-Instruct-Q4_K_M.gguf) | 379.38 MB |
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| Q4_K_S | [unsloth-Qwen2.5-Coder-0.5B-Instruct-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF/blob/main/unsloth-Qwen2.5-Coder-0.5B-Instruct-Q4_K_S.gguf) | 367.61 MB |
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| Q5_K_M | [unsloth-Qwen2.5-Coder-0.5B-Instruct-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF/blob/main/unsloth-Qwen2.5-Coder-0.5B-Instruct-Q5_K_M.gguf) | 400.63 MB |
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| Q5_K_S | [unsloth-Qwen2.5-Coder-0.5B-Instruct-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF/blob/main/unsloth-Qwen2.5-Coder-0.5B-Instruct-Q5_K_S.gguf) | 393.59 MB |
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| Q6_K | [unsloth-Qwen2.5-Coder-0.5B-Instruct-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF/blob/main/unsloth-Qwen2.5-Coder-0.5B-Instruct-Q6_K.gguf) | 482.31 MB |
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| Q8_0 | [unsloth-Qwen2.5-Coder-0.5B-Instruct-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/unsloth-Qwen2.5-Coder-0.5B-Instruct-GGUF/blob/main/unsloth-Qwen2.5-Coder-0.5B-Instruct-Q8_0.gguf) | 506.47 MB |
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---
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## β‘ Powered by [Featherless AI](https://featherless.ai)
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### Key Features
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- π₯ **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
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- π οΈ **Zero Infrastructure** - No server setup or maintenance required
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- π **Vast Compatibility** - Support for 2400+ models and counting
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- π **Affordable Pricing** - Starting at just $10/month
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---
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**Links:**
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[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
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version https://git-lfs.github.com/spec/v1
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