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

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

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 / README.md

boweizh1204

NOTICE: calibration used exactly five datasets (not Nemotron/SWE-ZERO/AgentTrove); exact Qwen copyright line; validator-measured bits. model.gguf unchanged

449619c verified 29 days ago

preview code

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metadata

license: apache-2.0
base_model: Qwen/Qwen3-8B
tags:
  - compression
  - quantization
  - gguf
  - bittensor
  - ralph

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 — 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: ≤4.0 code bits and ≤5.0 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 — attribution only).