Instructions to use nom666/Qwopus3.8-27B-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 nom666/Qwopus3.8-27B-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 nom666/Qwopus3.8-27B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf nom666/Qwopus3.8-27B-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 nom666/Qwopus3.8-27B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf nom666/Qwopus3.8-27B-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 nom666/Qwopus3.8-27B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf nom666/Qwopus3.8-27B-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 nom666/Qwopus3.8-27B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf nom666/Qwopus3.8-27B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/nom666/Qwopus3.8-27B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use nom666/Qwopus3.8-27B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nom666/Qwopus3.8-27B-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": "nom666/Qwopus3.8-27B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/nom666/Qwopus3.8-27B-GGUF:Q4_K_M
- Ollama
How to use nom666/Qwopus3.8-27B-GGUF with Ollama:
ollama run hf.co/nom666/Qwopus3.8-27B-GGUF:Q4_K_M
- Unsloth Studio
How to use nom666/Qwopus3.8-27B-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 nom666/Qwopus3.8-27B-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 nom666/Qwopus3.8-27B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for nom666/Qwopus3.8-27B-GGUF to start chatting
- Pi
How to use nom666/Qwopus3.8-27B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf nom666/Qwopus3.8-27B-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": "nom666/Qwopus3.8-27B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use nom666/Qwopus3.8-27B-GGUF with Docker Model Runner:
docker model run hf.co/nom666/Qwopus3.8-27B-GGUF:Q4_K_M
- Lemonade
How to use nom666/Qwopus3.8-27B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull nom666/Qwopus3.8-27B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwopus3.8-27B-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use nom666/Qwopus3.8-27B-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 nom666/Qwopus3.8-27B-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 nom666/Qwopus3.8-27B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use nom666/Qwopus3.8-27B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf nom666/Qwopus3.8-27B-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 "nom666/Qwopus3.8-27B-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"
Qwopus3.8-27B · GGUF (with MTP)
GGUF quants of Qwopus3.8-27B — a task-vector transplant that carries the Qwopus reasoning distillation onto the Qwen3.8-27B base. Qwopus3.8 keeps Qwen3.8's architecture, 262,144 context length and native MTP speculative head, and layers the Opus-style distilled reasoning trait on top: materially fewer tokens for the same result, and a stronger non-thinking (instruct) mode. The MTP block is exported (nextn layout), so llama.cpp speculative decoding works out of the box. MLX/Apple-Silicon users: see the MTPLX builds (4bit-Speed · 8bit-Quality).
Files
| File | Size | Notes |
|---|---|---|
Qwopus3.8-27B-Q4_K_M.gguf |
16 GB | fits 24 GB VRAM with room for context |
Qwopus3.8-27B-Q8_0.gguf |
27 GB | near-lossless; also the fastest llama.cpp quant on Apple Metal (K-quant kernels bottleneck there, not bandwidth) |
How it was made
Qwen3.6-27B and Qwen3.8-27B share an identical qwen3_5 skeleton (64 layers, 5120 hidden, 248K vocab, hybrid GDN + full attention, 1-layer MTP head), which makes cross-generation task arithmetic shape-exact:
Qwopus3.8 = Qwen3.8 + (Qwopus3.6-27B-v2 − Qwen3.6)
All 1,199 source tensors matched and merged (fp32 math, bf16 out), including the mtp.* head; converted with llama.cpp's convert_hf_to_gguf (MTP exported as nextn) and quantized with llama-quantize.
Results
Benchmarks below were measured on the MTPLX (MLX) builds of this same merge on an Apple M5 Max; GGUF quants at comparable bit-widths were smoke-tested for coherence and MTP drafting but not separately benchmarked.
Non-thinking mode (the recommended regime):
| Qwopus3.8 4-bit | Qwopus3.8 8-bit | Qwen3.8 8-bit | |
|---|---|---|---|
| 38-task instruct/code/math suite | 37/38 | 36/38 | 35/38 |
| Instruction-following subset | 4/5 | 3/5 | 2/5 |
| Hard-task suite (executed code + math + logic) | — | 8/13 @ 2,889 tok | 8/13 @ 3,384 tok |
Thinking mode — token efficiency: at high reasoning effort, Qwopus3.8 matches base Qwen3.8 on a 13-task hard suite (13/13 both) while spending 10,050 vs 18,455 reasoning tokens — 45% fewer.
Usage
llama-server -m Qwopus3.8-27B-Q4_K_M.gguf -ngl 99 -c 32768 \
--alias qwopus38 --spec-type draft-mtp --spec-draft-n-max 2 \
--temp 0.3 --top-p 0.9 --top-k 40
Disable thinking (recommended for agentic/coding loops) via your chat-template kwargs or client; for thinking mode use a generous max_tokens. Do not greedy-decode in thinking mode (a known Qwen-family failure pattern).
Notes
- Recommended for fast agentic/coding loops and instruct-style serving with thinking disabled — that is where this merge measurably beats its base.
- With thinking enabled, Qwopus3.8 solves hard tasks with ~45% fewer reasoning tokens, though base Qwen3.8 remains the stronger choice for heavy thinking-mode workloads — it is more consistent at concluding very long reasoning inside tight token budgets (Qwopus3.8 can occasionally deliberate past a tight budget on some prompts) and keeps an edge on the hardest reasoning tasks.
- This is a weight-space merge, not a fine-tune: no gradient training was performed on Qwen3.8.
- All credit for the Qwopus distillation to Jackrong (Qwopus3.6-27B-v2); base model Qwen3.8-27B by the Qwen team.
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