Instructions to use boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910 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 boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910 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 boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910 # Run inference directly in the terminal: llama cli -hf boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910 # Run inference directly in the terminal: llama cli -hf boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910
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 boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910 # Run inference directly in the terminal: ./llama-cli -hf boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910
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 boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910 # Run inference directly in the terminal: ./build/bin/llama-cli -hf boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910
Use Docker
docker model run hf.co/boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910
- LM Studio
- Jan
- Ollama
How to use boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910 with Ollama:
ollama run hf.co/boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910
- Unsloth Desktop
- Pi
How to use boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910
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": "boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910 with Docker Model Runner:
docker model run hf.co/boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910
- Lemonade
How to use boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910
Run and chat with the model
lemonade run user.qwen3-8b-ralph-sub4-c1b-20260910-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910
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 boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910
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 "boweizh1204/qwen3-8b-ralph-sub4-c1b-20260910" \ --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"
qwen3-8b-ralph-sub4-c1b-20260910
Compressed derivative of Qwen/Qwen3-8B submitted to the sub4 bit-tier of Bittensor subnet 40 (Ralph, model compression). Architecture and parameter count are unchanged from the parent; weights are re-stored at reduced bit-width.
License
Released under the Apache License 2.0 โ see LICENSE. This is a derivative of Qwen3-8B, itself Apache-2.0;
the parent's copyright and attribution notices, a description of what changed, the data used and the third-party
tooling involved are preserved in NOTICE. The exact artifact is model.gguf at revision 3c21306c630dac6440b37ad67b965a4b9b787cf9
(SHA-256 b2ec80dce90258bbe9555a67983dea595d91f35cae48ca0d54c714eae8100edf); adding this model card and license files does not alter that file.
Model overview
| Parent | Qwen/Qwen3-8B (Apache-2.0) |
| Architecture | unchanged from parent |
| Format | GGUF (llama.cpp) |
| Bit tier | sub4 (gate: โค4.0 code bits and โค5.0 container bits per weight) |
| File size | 4,614,304,896 bytes |
| Measured bits/weight | 4.0 code bits, 4.5 container bits (validator-measured) |
| Method | Post-training weight re-storage of the parent's own weights: llama.cpp pure Q4_K quantization with an importance-matrix (imatrix) calibration computed on the bf16 parent at 2048-token context over an exam-proportional sample of public trajectory datasets. No retraining, distillation or architectural change. |
Upstream terms to preserve: Qwen3-8B's Apache-2.0 license and attribution (kept in NOTICE); llama.cpp's MIT license
for the GGUF tooling; the dataset licenses listed in NOTICE (Apache-2.0, MIT, CC-BY-4.0 โ attribution only).
- Downloads last month
- 182
We're not able to determine the quantization variants.