Text Generation
GGUF
English
Chinese
uncensored
abliterated
qwen3.8
imatrix
dynamic-quant
conversational
Instructions to use outsourc-e/Qwen3.8-27B-Unleashed-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 outsourc-e/Qwen3.8-27B-Unleashed-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 outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M # Run inference directly in the terminal: llama cli -hf outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M # Run inference directly in the terminal: llama cli -hf outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-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 outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M
Use Docker
docker model run hf.co/outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use outsourc-e/Qwen3.8-27B-Unleashed-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "outsourc-e/Qwen3.8-27B-Unleashed-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": "outsourc-e/Qwen3.8-27B-Unleashed-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M
- Ollama
How to use outsourc-e/Qwen3.8-27B-Unleashed-GGUF with Ollama:
ollama run hf.co/outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M
- Unsloth Desktop
- Pi
How to use outsourc-e/Qwen3.8-27B-Unleashed-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-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": "outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use outsourc-e/Qwen3.8-27B-Unleashed-GGUF with Docker Model Runner:
docker model run hf.co/outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M
- Lemonade
How to use outsourc-e/Qwen3.8-27B-Unleashed-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M
Run and chat with the model
lemonade run user.Qwen3.8-27B-Unleashed-GGUF-UD-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use outsourc-e/Qwen3.8-27B-Unleashed-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 outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-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 outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use outsourc-e/Qwen3.8-27B-Unleashed-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-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 "outsourc-e/Qwen3.8-27B-Unleashed-GGUF:UD-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"
Commit History
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Complete 9-tier ladder; IQ1_M measured at random chance (25.83% MMLU) - documented, not hidden
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Charts rebuilt with full 9-tier data incl. IQ1_M collapse
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Restore all 9 tiers; document the real low-bit build fix (prune + override-kv)
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Rebuild charts from 2026-08-21 9-tier sweep
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Upload Qwen3.8-27B-Unleashed-UD-IQ2_S.gguf with huggingface_hub
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Upload Qwen3.8-27B-Unleashed-UD-IQ1_M.gguf with huggingface_hub
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Replace inflated speed claims with measured n=1696 distribution by generation length
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Withdraw IQ1_M + IQ2_S: valid header but fail to load (prune-layers 64 strips blk.63.nextn)
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IQ2_S: same defect as IQ1_M - valid header, fails to load. Withdrawing.
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IQ1_M: valid header but fails to load (blk.63.nextn.eh_proj missing) - withdrawing again
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All 10 files verified; vision projector bundled + tested (3/3 gates); remove withdrawal notice
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Upload mmproj-Unleashed-f16.gguf with huggingface_hub
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Upload Qwen3.8-27B-Unleashed-UD-IQ1_M.gguf with huggingface_hub
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Upload Qwen3.8-27B-Unleashed-UD-IQ1_M.gguf with huggingface_hub
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Add 7-tier benchmark ladder (PPL/speed/MMLU/refusal), PPL-vs-capability finding, abliteration-method comparison; correct speed claim; IQ2_S restored
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Upload Qwen3.8-27B-Unleashed-UD-IQ2_S.gguf with huggingface_hub
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Notice: IQ1_M/IQ2_S withdrawn pending rebuild
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Remove corrupt file (zeroed header) - rebuilding
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Remove corrupt file (zeroed header) - rebuilding
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Add UD-IQ1_M
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Expand DFlash2 speculative decoding section
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Add vision setup instructions
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Add refusal rate 0/20, MTP + vision notes
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Fact-check: add IQ4_XS/IQ3_XXS PPL, correct size deltas
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Add measured MMLU 82.98%
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Add file/VRAM table, quick start, inline charts
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Add base-model context + optimal settings
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Add UD-Q6_K
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Add UD-Q5_K_M
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Add UD-Q4_K_M
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Add UD-Q2_K_XL
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Caps title
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Add UD-IQ1_M
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Add UD-IQ2_S
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Add benchmark charts
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Add benchmark charts
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Add benchmark charts
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Add benchmark charts
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Add UD-IQ3_XXS
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Dragon banner + BenchLoop ref
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Dragon banner
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Add UD-IQ4_XS
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Add banner + badges
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Add card banner
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Add UD-Q3_K_XL (PPL 6.4036, 250k retrieval verified)
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Add model card
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initial commit
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