Instructions to use reaperdoesntknow/qwen3.5-4B-Code-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use reaperdoesntknow/qwen3.5-4B-Code-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 reaperdoesntknow/qwen3.5-4B-Code-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf reaperdoesntknow/qwen3.5-4B-Code-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 reaperdoesntknow/qwen3.5-4B-Code-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf reaperdoesntknow/qwen3.5-4B-Code-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf reaperdoesntknow/qwen3.5-4B-Code-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf reaperdoesntknow/qwen3.5-4B-Code-GGUF:Q4_K_M
Use Docker
docker model run hf.co/reaperdoesntknow/qwen3.5-4B-Code-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use reaperdoesntknow/qwen3.5-4B-Code-GGUF with Ollama:
ollama run hf.co/reaperdoesntknow/qwen3.5-4B-Code-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use reaperdoesntknow/qwen3.5-4B-Code-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf reaperdoesntknow/qwen3.5-4B-Code-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "reaperdoesntknow/qwen3.5-4B-Code-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use reaperdoesntknow/qwen3.5-4B-Code-GGUF with Docker Model Runner:
docker model run hf.co/reaperdoesntknow/qwen3.5-4B-Code-GGUF:Q4_K_M
- Lemonade
How to use reaperdoesntknow/qwen3.5-4B-Code-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull reaperdoesntknow/qwen3.5-4B-Code-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.qwen3.5-4B-Code-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use reaperdoesntknow/qwen3.5-4B-Code-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 reaperdoesntknow/qwen3.5-4B-Code-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 reaperdoesntknow/qwen3.5-4B-Code-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use reaperdoesntknow/qwen3.5-4B-Code-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf reaperdoesntknow/qwen3.5-4B-Code-GGUF:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "reaperdoesntknow/qwen3.5-4B-Code-GGUF:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf reaperdoesntknow/qwen3.5-4B-Code-GGUF:# Run inference directly in the terminal:
llama cli -hf reaperdoesntknow/qwen3.5-4B-Code-GGUF:Build from source code
git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf reaperdoesntknow/qwen3.5-4B-Code-GGUF:# Run inference directly in the terminal:
./build/bin/llama cli -hf reaperdoesntknow/qwen3.5-4B-Code-GGUF:Use Docker
docker model run hf.co/reaperdoesntknow/qwen3.5-4B-Code-GGUF:qwen3.5-4B-Code-GGUF
GGUF files for a Qwen3.5 4B coding-oriented release, published by reaperdoesntknow. The original export card states that conversion used Unsloth.
Available files
| File | Role |
|---|---|
| Qwen3.5-4B.Q4_0.gguf | Q4_0 language-model quantization |
| Qwen3.5-4B.Q4_K_M.gguf | Q4_K_M language-model quantization |
| Qwen3.5-4B.Q8_0.gguf | Q8_0 language-model quantization |
| Qwen3.5-4B.BF16-mmproj.gguf | Multimodal projector; not a standalone language model |
Choose one language-model file for your runtime. For image inputs, a compatible multimodal runtime also needs the matching projector. The quantization labels above follow the published filenames; this card does not report a tensor-level audit or measured quality comparison.
Usage
Download your selected file from the Files tab, then load it in a current GGUF runtime that explicitly supports Qwen3.5. For llama.cpp, consult the official usage documentation and multimodal documentation.
Select the desired filename explicitly rather than relying on automatic selection among the three quantizations. For multimodal use, pair it with Qwen3.5-4B.BF16-mmproj.gguf. The projector cannot replace the main model file.
The original card supplied llama-cli and llama-mtmd-cli examples using this repository ID and --jinja. Exact command support depends on the installed runtime revision. No inference smoke test or hardware-specific memory requirement is documented for these exports.
Provenance
The included config.json identifies Qwen3_5ForConditionalGeneration with text and vision components. Its text configuration specifies bfloat16; that does not describe the storage precision of the quantized GGUF files.
A related Transformers repository is Qwen3.5-4B-Coder. The original GGUF card does not identify an exact source checkpoint revision, conversion command, or quantization tool revision. An exact conversion relationship is therefore not asserted here.
The existing GGUF metadata does not declare a license or base model. Those fields have not been inferred or copied from a similarly named repository. Confirm applicable source-model terms before redistribution or commercial use.
Evaluation and limitations
The published files reviewed for this card do not include training-data documentation, a coding benchmark, a comparison between quantizations, or a safety evaluation. A coding-oriented name and successful conversion do not establish improved coding ability or tested multimodal performance.
Use for local experimentation and evaluation. Generated code may contain errors or vulnerabilities; review and test it before execution. Do not expose secrets or production systems to unreviewed generated code. Quantization can change outputs, so evaluate the exact file and runtime you intend to use.
- Downloads last month
- 1,447
4-bit
8-bit
Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf reaperdoesntknow/qwen3.5-4B-Code-GGUF:# Run inference directly in the terminal: llama cli -hf reaperdoesntknow/qwen3.5-4B-Code-GGUF: