Instructions to use ArizonaZZZ/ralph-v2-qwen3-8b-binary-p2A2-step500-05c56816 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ArizonaZZZ/ralph-v2-qwen3-8b-binary-p2A2-step500-05c56816 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 ArizonaZZZ/ralph-v2-qwen3-8b-binary-p2A2-step500-05c56816
# Run inference directly in the terminal:
llama cli -hf ArizonaZZZ/ralph-v2-qwen3-8b-binary-p2A2-step500-05c56816

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ArizonaZZZ/ralph-v2-qwen3-8b-binary-p2A2-step500-05c56816
# Run inference directly in the terminal:
llama cli -hf ArizonaZZZ/ralph-v2-qwen3-8b-binary-p2A2-step500-05c56816

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 ArizonaZZZ/ralph-v2-qwen3-8b-binary-p2A2-step500-05c56816
# Run inference directly in the terminal:
./llama-cli -hf ArizonaZZZ/ralph-v2-qwen3-8b-binary-p2A2-step500-05c56816

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 ArizonaZZZ/ralph-v2-qwen3-8b-binary-p2A2-step500-05c56816
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ArizonaZZZ/ralph-v2-qwen3-8b-binary-p2A2-step500-05c56816

Use Docker

docker model run hf.co/ArizonaZZZ/ralph-v2-qwen3-8b-binary-p2A2-step500-05c56816

LM Studio
Jan
Ollama
How to use ArizonaZZZ/ralph-v2-qwen3-8b-binary-p2A2-step500-05c56816 with Ollama:
```
ollama run hf.co/ArizonaZZZ/ralph-v2-qwen3-8b-binary-p2A2-step500-05c56816
```
Unsloth Desktop
Docker Model Runner
How to use ArizonaZZZ/ralph-v2-qwen3-8b-binary-p2A2-step500-05c56816 with Docker Model Runner:
```
docker model run hf.co/ArizonaZZZ/ralph-v2-qwen3-8b-binary-p2A2-step500-05c56816
```

Lemonade

How to use ArizonaZZZ/ralph-v2-qwen3-8b-binary-p2A2-step500-05c56816 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ArizonaZZZ/ralph-v2-qwen3-8b-binary-p2A2-step500-05c56816

Run and chat with the model

lemonade run user.ralph-v2-qwen3-8b-binary-p2A2-step500-05c56816-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

ralph-v2-qwen3-8b-binary-p2A2-step500-05c56816 / README.md

ArizonaZZZ

Add Apache-2.0 license, model card and NOTICE (weights unchanged)

74bb241 verified 28 days ago

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metadata

license: apache-2.0
base_model:
  - prism-ml/Bonsai-8B-unpacked
  - Qwen/Qwen3-8B
tags:
  - gguf
  - q1_0
  - 1-bit
  - qwen3
  - bonsai
  - bittensor
  - sn40
language:
  - en
  - hi
  - zh
library_name: llama.cpp

ralph-v2-qwen3-8b-binary-p2A2-step500

A 1-bit (Q1_0 GGUF, 1.0 code bits / 1.125 container bits per weight) Qwen3-8B-architecture model, the SN40 (Ralph v2) binary-tier crown from round 7.

License: Apache-2.0 — for the exact file model.gguf in this repository (sha256 05c568169fc180067172cbdd38c1a9f5c249a556ffdef1dacd7172bad40fab58, chain-pinned revision 29c525c5c3166df2d62d5e180d09255199f778fe).

Lineage and upstream terms to preserve

component	source	license	what we preserve
binary weights (signs)	prism-ml/Bonsai-8B-unpacked (PrismML)	Apache-2.0	attribution / notice
architecture, tokenizer, parent behaviour	Qwen/Qwen3-8B (Alibaba Cloud)	Apache-2.0 (LICENSE)	attribution / notice
container / kernels	llama.cpp GGUF Q1_0	MIT (tooling; not embedded)	—

Training data used to fit the per-block scales and norms (no dataset text is embedded in the weights): prompts drawn from public datasets, with targets generated by Qwen/Qwen3-8B.

dataset	license	note
nvidia/OpenMathReasoning	CC-BY-4.0	attribution required
zake7749/OpenScience-Chinese-Reasoning-SFT	CC-BY-4.0	attribution required
glaiveai/reasoning-v1-20m	Apache-2.0
sarvamai/samvaad-hi-v1	Apache-2.0
ricdomolm/mini-coder-trajs-400k	MIT

Method (brief)

Bonsai-8B's 1-bit signs are kept bit-exact; only the 128-element group scales and the RMSNorm weights were re-fitted by distillation from Qwen3-8B's own generated continuations (KL(parent‖student) on parent-step tokens, with extra weight on the first tokens of each step and an unlikelihood penalty on chat-template leak tokens), then exported losslessly to Q1_0. Selection used a 720-item private pool scored under three observers. Contains no auto_map, no code, and no dataset text.

ArizonaZZZ
/

ralph-v2-qwen3-8b-binary-p2A2-step500-05c56816

ralph-v2-qwen3-8b-binary-p2A2-step500

Lineage and upstream terms to preserve

Method (brief)

Attribution