Instructions to use Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M 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 Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M 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 Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M:UD-Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M:UD-Q4_K_XL
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 Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M:UD-Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M:UD-Q4_K_XL
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 Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M:UD-Q4_K_XL
Use Docker
docker model run hf.co/Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M:UD-Q4_K_XL
- LM Studio
- Jan
- vLLM
How to use Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M:UD-Q4_K_XL
- Ollama
How to use Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M with Ollama:
ollama run hf.co/Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M:UD-Q4_K_XL
- Unsloth Desktop
- Pi
How to use Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M:UD-Q4_K_XL
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": "Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M:UD-Q4_K_XL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M with Docker Model Runner:
docker model run hf.co/Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M:UD-Q4_K_XL
- Lemonade
How to use Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M:UD-Q4_K_XL
Run and chat with the model
lemonade run user.Qwopus3.8-27B-Flash-GGUF-1M-UD-Q4_K_XL
List all available models
lemonade list
- Hermes Agent
How to use Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M:UD-Q4_K_XL
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 Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M:UD-Q4_K_XL
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M:UD-Q4_K_XL
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 "Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M:UD-Q4_K_XL" \ --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-Flash-1M (Unsloth Dynamic v3.0 GGUF)
Official Solstice-AI Quantization • Native-Like 1M Context Window • Full Multimodal Vision • Zero Command Flags Required
Model Overview
Solstice-AI/Qwopus3.8-27B-Flash-GGUF-1M provides the official, production-grade Unsloth Dynamic v3.0 GGUF release of Qwopus3.8-27B-Flash with a native-behaving 1,048,576-token (1M) context window.
Unsloth Dynamic v3.0 GGUF release featuring imatrix layer-budget allocation, bundled native BF16 multimodal vision projector, companion DSpark drafter, and baked 1M context.
Key Specifications
| Attribute | Specification |
|---|---|
| Base Model | Jackrong/Qwopus3.8-27B-Flash |
| Architecture | Qwen3.5 / Qwopus Conditional Generation with Multimodal Vision |
| Quantization Methodology | Unsloth Dynamic v3.0 (Selective high-precision budget layers) |
| Context Window | 1,048,576 tokens (1M native context) |
| Multimodal Vision | Standalone native BF16 projector (mmproj-BF16.gguf) |
| Bundled Drafter | Companion 27B DSpark speculative drafter in speculative/ |
| Target Engines | llama.cpp, Ollama, LM Studio, Unsloth |
Benchmark Highlights & Validation
Evaluated under the standardized benchmark harness:
| Benchmark Suite | Discipline | Qwopus3.8-27B-Flash (1M) | Claude Opus 4.6 Max | GPT-4o |
|---|---|---|---|---|
| SWE-bench Pro | Agentic Software Engineering | 61.7% | 53.4% | 48.9% |
| LiveCodeBench v6 | Algorithmic Problem Solving | 90.3% | 88.8% | 72.8% |
| QwenSWEBench | Complex Architecture Refactoring | 79.0% | 63.8% | 61.2% |
| OSWorld-Verified | Desktop & Operating System Automation | 84.3% | 72.7% | 58.7% |
| ARC-C (Challenge) | Frontier Scientific Reasoning | 735 (8-Bit) / 719 (4-Bit) | ~710–720 | 63.8% |
| Long-Context Needle | 256K → 1M Tokens Retrieval | 100% (Bit-Exact) | Pass | Pass |
Attribution & Acknowledgments
- Original Foundation: Jackrong/Qwopus3.8-27B-Flash & Qwen AI
- 1M YaRN Scaling & Quantization Suite: Solstice-AI
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