Instructions to use magiccodingman/Qwen3.6-35B-A3B-MagicQuant-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 magiccodingman/Qwen3.6-35B-A3B-MagicQuant-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 magiccodingman/Qwen3.6-35B-A3B-MagicQuant-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf magiccodingman/Qwen3.6-35B-A3B-MagicQuant-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 magiccodingman/Qwen3.6-35B-A3B-MagicQuant-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf magiccodingman/Qwen3.6-35B-A3B-MagicQuant-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 magiccodingman/Qwen3.6-35B-A3B-MagicQuant-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf magiccodingman/Qwen3.6-35B-A3B-MagicQuant-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 magiccodingman/Qwen3.6-35B-A3B-MagicQuant-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf magiccodingman/Qwen3.6-35B-A3B-MagicQuant-GGUF:Q4_K_M
Use Docker
docker model run hf.co/magiccodingman/Qwen3.6-35B-A3B-MagicQuant-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use magiccodingman/Qwen3.6-35B-A3B-MagicQuant-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "magiccodingman/Qwen3.6-35B-A3B-MagicQuant-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": "magiccodingman/Qwen3.6-35B-A3B-MagicQuant-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/magiccodingman/Qwen3.6-35B-A3B-MagicQuant-GGUF:Q4_K_M
- Ollama
How to use magiccodingman/Qwen3.6-35B-A3B-MagicQuant-GGUF with Ollama:
ollama run hf.co/magiccodingman/Qwen3.6-35B-A3B-MagicQuant-GGUF:Q4_K_M
- Unsloth Studio
How to use magiccodingman/Qwen3.6-35B-A3B-MagicQuant-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 magiccodingman/Qwen3.6-35B-A3B-MagicQuant-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 magiccodingman/Qwen3.6-35B-A3B-MagicQuant-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for magiccodingman/Qwen3.6-35B-A3B-MagicQuant-GGUF to start chatting
- Pi
How to use magiccodingman/Qwen3.6-35B-A3B-MagicQuant-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf magiccodingman/Qwen3.6-35B-A3B-MagicQuant-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": "magiccodingman/Qwen3.6-35B-A3B-MagicQuant-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use magiccodingman/Qwen3.6-35B-A3B-MagicQuant-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 magiccodingman/Qwen3.6-35B-A3B-MagicQuant-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 magiccodingman/Qwen3.6-35B-A3B-MagicQuant-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use magiccodingman/Qwen3.6-35B-A3B-MagicQuant-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf magiccodingman/Qwen3.6-35B-A3B-MagicQuant-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 "magiccodingman/Qwen3.6-35B-A3B-MagicQuant-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"
- Docker Model Runner
How to use magiccodingman/Qwen3.6-35B-A3B-MagicQuant-GGUF with Docker Model Runner:
docker model run hf.co/magiccodingman/Qwen3.6-35B-A3B-MagicQuant-GGUF:Q4_K_M
- Lemonade
How to use magiccodingman/Qwen3.6-35B-A3B-MagicQuant-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull magiccodingman/Qwen3.6-35B-A3B-MagicQuant-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3.6-35B-A3B-MagicQuant-GGUF-Q4_K_M
List all available models
lemonade list
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| "key": "118:0:0:0:0:0:0:0:0:0", | |
| "fileName": "Qwen3.6-35B-A3B-UD-Q6_K_XL.gguf", | |
| "displayName": "Qwen3.6-35B-A3B-UD-Q6_K_XL", | |
| "shortName": "UD-Q6_K_XL", | |
| "provider": "Unsloth", | |
| "quantFamily": "UD-Q6_K_XL", | |
| "isHybrid": false, | |
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| "displayName": "MQ-Q6_K_1", | |
| "shortName": "MQ-Q6_K_1", | |
| "provider": "MagicQuant", | |
| "quantFamily": "Q6_K", | |
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| }, | |
| { | |
| "reasonCode": "STRICT_DOMINANCE", | |
| "reasonDescription": "The winner was no larger and had lower real KLD than the removed anchor.", | |
| "rawReason": "smart baseline-tuning strict dominance fallback: real benchmark validated lower KLD at same-or-smaller size", | |
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| "key": "117:0:0:0:0:0:0:0:0:0", | |
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| "displayName": "Qwen3.6-35B-A3B-UD-Q6_K", | |
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| "provider": "Unsloth", | |
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| "displayName": "MQ-Q5_K_1", | |
| "shortName": "MQ-Q5_K_1", | |
| "provider": "MagicQuant", | |
| "quantFamily": "Q5_K", | |
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| }, | |
| { | |
| "reasonCode": "STRICT_DOMINANCE", | |
| "reasonDescription": "The winner was no larger and had lower real KLD than the removed anchor.", | |
| "rawReason": "strict predicted hybrid dominance validated by real benchmark", | |
| "removed": { | |
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| "fileName": "Qwen3.6-35B-A3B-MQ-Q5_K_S.gguf", | |
| "displayName": "Qwen3.6-35B-A3B-MQ-Q5_K_S", | |
| "shortName": "MQ-UD-Q5_K_XL", | |
| "provider": "MagicQuant", | |
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| "displayName": "MQ-Q5_K_S_1", | |
| "shortName": "MQ-Q5_K_S_1", | |
| "provider": "MagicQuant", | |
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| { | |
| "reasonCode": "STRICT_DOMINANCE", | |
| "reasonDescription": "The winner was no larger and had lower real KLD than the removed anchor.", | |
| "rawReason": "smart baseline-tuning strict dominance fallback: real benchmark validated lower KLD at same-or-smaller size", | |
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| "key": "116:0:0:0:0:0:0:0:0:0", | |
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| "displayName": "Qwen3.6-35B-A3B-UD-Q5_K_XL", | |
| "shortName": "UD-Q5_K_XL", | |
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| "displayName": "Qwen3.6-35B-A3B-MQ-Q5_K_S", | |
| "shortName": "MQ-UD-Q5_K_XL", | |
| "provider": "MagicQuant", | |
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| { | |
| "reasonCode": "STRICT_DOMINANCE", | |
| "reasonDescription": "The winner was no larger and had lower real KLD than the removed anchor.", | |
| "rawReason": "strict predicted hybrid dominance validated by real benchmark", | |
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| "key": "116:0:0:0:0:0:0:0:0:0", | |
| "fileName": "Qwen3.6-35B-A3B-UD-Q5_K_XL.gguf", | |
| "displayName": "Qwen3.6-35B-A3B-UD-Q5_K_XL", | |
| "shortName": "UD-Q5_K_XL", | |
| "provider": "Unsloth", | |
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| "displayName": "MQ-Q5_K_S_1", | |
| "shortName": "MQ-Q5_K_S_1", | |
| "provider": "MagicQuant", | |
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| } | |
| }, | |
| { | |
| "reasonCode": "STRICT_DOMINANCE", | |
| "reasonDescription": "The winner was no larger and had lower real KLD than the removed anchor.", | |
| "rawReason": "smart baseline-tuning strict dominance fallback: real benchmark validated lower KLD at same-or-smaller size", | |
| "removed": { | |
| "key": "114:0:0:0:0:0:0:0:0:0", | |
| "fileName": "Qwen3.6-35B-A3B-UD-Q5_K_M.gguf", | |
| "displayName": "Qwen3.6-35B-A3B-UD-Q5_K_M", | |
| "shortName": "UD-Q5_K_M", | |
| "provider": "Unsloth", | |
| "quantFamily": "UD-Q5_K_M", | |
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| "winner": { | |
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| "fileName": "Qwen3.6-35B-A3B-MQ-Q5_K_S_1.gguf", | |
| "displayName": "MQ-Q5_K_S_1", | |
| "shortName": "MQ-Q5_K_S_1", | |
| "provider": "MagicQuant", | |
| "quantFamily": "Q5_K_S", | |
| "isHybrid": true, | |
| "isExternalPureBaseline": false, | |
| "isExternalRebuiltBaseline": false, | |
| "isMaterializedTensorMapped": true, | |
| "kld": 0.00673, | |
| "ppl": 5.679092, | |
| "pplDeltaPercent": -0.1390539827677241, | |
| "sizeBytes": 26333026720, | |
| "sizeGB": 26.33302672, | |
| "sizeGiB": 24.52454224228859 | |
| }, | |
| "deltas": { | |
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| "winnerPplDeltaPercent": -0.1390539827677241, | |
| "pplDeltaPercentImprovement": 0.001389133110609897 | |
| } | |
| }, | |
| { | |
| "reasonCode": "STRICT_DOMINANCE", | |
| "reasonDescription": "The winner was no larger and had lower real KLD than the removed anchor.", | |
| "rawReason": "smart baseline-tuning strict dominance fallback: real benchmark validated lower KLD at same-or-smaller size", | |
| "removed": { | |
| "key": "115:0:0:0:0:0:0:0:0:0", | |
| "fileName": "Qwen3.6-35B-A3B-UD-Q5_K_S.gguf", | |
| "displayName": "Qwen3.6-35B-A3B-UD-Q5_K_S", | |
| "shortName": "UD-Q5_K_S", | |
| "provider": "Unsloth", | |
| "quantFamily": "UD-Q5_K_S", | |
| "isHybrid": false, | |
| "isExternalPureBaseline": true, | |
| "isExternalRebuiltBaseline": false, | |
| "isMaterializedTensorMapped": false, | |
| "kld": 0.007824, | |
| "ppl": 5.692164, | |
| "pplDeltaPercent": 0.09080358712853392, | |
| "sizeBytes": 24942049696, | |
| "sizeGB": 24.942049695999998, | |
| "sizeGiB": 23.229093939065933 | |
| }, | |
| "winner": { | |
| "key": "115:103:116:116:116:116:116:116:0:116", | |
| "fileName": "Qwen3.6-35B-A3B-MQ-Q4_K_M_1.gguf", | |
| "displayName": "MQ-Q4_K_M_1", | |
| "shortName": "MQ-Q4_K_M_1", | |
| "provider": "MagicQuant", | |
| "quantFamily": "Q4_K_M", | |
| "isHybrid": true, | |
| "isExternalPureBaseline": false, | |
| "isExternalRebuiltBaseline": false, | |
| "isMaterializedTensorMapped": true, | |
| "kld": 0.007799, | |
| "ppl": 5.685451, | |
| "pplDeltaPercent": -0.027237559345888666, | |
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| "reasonDescription": "Two candidates were too close in practical output space; the stronger one was kept.", | |
| "rawReason": "meaningful spacing collapse; size gap below 822,164,582 bytes", | |
| "removed": { | |
| "key": "12:0:0:0:0:0:0:0:0:0", | |
| "fileName": "Qwen3.6-35B-A3B-LM-IQ2_XXS.gguf", | |
| "displayName": "Qwen3.6-35B-A3B-LM-IQ2_XXS", | |
| "shortName": "LM-IQ2_XXS", | |
| "provider": "llama.cpp", | |
| "quantFamily": "IQ2_XXS", | |
| "isHybrid": false, | |
| "isExternalPureBaseline": false, | |
| "isExternalRebuiltBaseline": false, | |
| "isMaterializedTensorMapped": false, | |
| "kld": 0.344116, | |
| "ppl": 7.511203, | |
| "pplDeltaPercent": 32.076718832424824, | |
| "sizeBytes": 9497653664, | |
| "sizeGB": 9.497653664000001, | |
| "sizeGiB": 8.845379263162613 | |
| }, | |
| "winner": { | |
| "key": "12:13:13:15:18:7:13:13:0:13", | |
| "fileName": "Qwen3.6-35B-A3B-MQ-IQ2_XXS_1.gguf", | |
| "displayName": "MQ-IQ2_XXS_1", | |
| "shortName": "MQ-IQ2_XXS_1", | |
| "provider": "MagicQuant", | |
| "quantFamily": "IQ2_XXS", | |
| "isHybrid": true, | |
| "isExternalPureBaseline": false, | |
| "isExternalRebuiltBaseline": false, | |
| "isMaterializedTensorMapped": true, | |
| "kld": 0.27513, | |
| "ppl": 7.049514, | |
| "pplDeltaPercent": 23.958396342535607, | |
| "sizeBytes": 9587160480, | |
| "sizeGB": 9.58716048, | |
| "sizeGiB": 8.928738981485367 | |
| }, | |
| "deltas": { | |
| "kld": 0.06898599999999999, | |
| "sizeBytes": -89506816, | |
| "sizeGB": -0.089506816, | |
| "sizeGiB": -0.0833597183227539, | |
| "removedPplDeltaPercent": 32.076718832424824, | |
| "winnerPplDeltaPercent": 23.958396342535607, | |
| "pplDeltaPercentImprovement": 8.118322489889216 | |
| } | |
| } | |
| ] |