Instructions to use boweizh1204/ralph-qwen3-8b-sub2-championship-final 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/ralph-qwen3-8b-sub2-championship-final 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/ralph-qwen3-8b-sub2-championship-final # Run inference directly in the terminal: llama cli -hf boweizh1204/ralph-qwen3-8b-sub2-championship-final
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf boweizh1204/ralph-qwen3-8b-sub2-championship-final # Run inference directly in the terminal: llama cli -hf boweizh1204/ralph-qwen3-8b-sub2-championship-final
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/ralph-qwen3-8b-sub2-championship-final # Run inference directly in the terminal: ./llama-cli -hf boweizh1204/ralph-qwen3-8b-sub2-championship-final
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/ralph-qwen3-8b-sub2-championship-final # Run inference directly in the terminal: ./build/bin/llama-cli -hf boweizh1204/ralph-qwen3-8b-sub2-championship-final
Use Docker
docker model run hf.co/boweizh1204/ralph-qwen3-8b-sub2-championship-final
- LM Studio
- Jan
- Ollama
How to use boweizh1204/ralph-qwen3-8b-sub2-championship-final with Ollama:
ollama run hf.co/boweizh1204/ralph-qwen3-8b-sub2-championship-final
- Unsloth Desktop
- Pi
How to use boweizh1204/ralph-qwen3-8b-sub2-championship-final with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf boweizh1204/ralph-qwen3-8b-sub2-championship-final
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/ralph-qwen3-8b-sub2-championship-final" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use boweizh1204/ralph-qwen3-8b-sub2-championship-final with Docker Model Runner:
docker model run hf.co/boweizh1204/ralph-qwen3-8b-sub2-championship-final
- Lemonade
How to use boweizh1204/ralph-qwen3-8b-sub2-championship-final with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull boweizh1204/ralph-qwen3-8b-sub2-championship-final
Run and chat with the model
lemonade run user.ralph-qwen3-8b-sub2-championship-final-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use boweizh1204/ralph-qwen3-8b-sub2-championship-final 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/ralph-qwen3-8b-sub2-championship-final
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/ralph-qwen3-8b-sub2-championship-final
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use boweizh1204/ralph-qwen3-8b-sub2-championship-final with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf boweizh1204/ralph-qwen3-8b-sub2-championship-final
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/ralph-qwen3-8b-sub2-championship-final" \ --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 v2 sub2 โ championship hybrid v1
Miner-produced sub2 candidate for Ralph v2 on Bittensor netuid 40. This
repository contains one behavior-bearing GGUF plus its validator-compatible
manifest. It is isolated from the author's pre-existing sub4 artifact.
Immutable parent and training lineage
- Parent:
Qwen/Qwen3-8B@b968826d9c46dd6066d109eabc6255188de91218 - Architecture: Qwen3, 36 layers, hidden size 4096, vocabulary size 151936
- Validator-measured weight elements: 8,190,427,136
- Training: rank-32 QLoRA, alpha 64, dropout 0.05, one epoch, effective batch 16
- Targets: attention q/k/v/o and MLP gate/up/down projections
- Merge point: update 27 of 54 (the half checkpoint outperformed the final checkpoint after low-bit quantization)
- Validation loss: 0.583881 before training; 0.100861 at the final adapter
The training set contains 864 custom parent-distilled examples, with a separate 96-example validation split. It is balanced across English, Hindi, and Chinese; shallow and deep reasoning; and math, code, dialogue, and agentic tasks. Published Ralph scored-pool items were used only as held-out diagnostics and were not used for training or calibration.
Importance calibration and quantization
The importance matrix was produced from 144 custom distilled examples: exactly six per language ร depth ร task cell, totaling 70,018 tokens and 34 chunks of 2,048 tokens.
- Attention projection matrices:
Q2_K - Feed-forward matrices and output head: true llama.cpp
IQ2_S - Token embedding:
Q2_K - Logical code bits/weight: 2.0000
- Physical container bits/weight: 2.5625
- Serialized package size: 2,647,344,636 bytes
- Tensor histogram:
IQ2_S=6,058,147,840,Q2_K=2,132,279,296,F32=308,224 - Four-chunk calibration perplexity: 2.7210
Alternative pure IQ2_S, IQ2_XS, IQ2_XXS, TQ2_0, and importance-mixed layouts were screened. The attention-Q2_K hybrid produced the strongest signed-round Phi diagnostic while remaining inside both current sub2 limits.
The tuned all-IQ2_S finalist was then compared directly with the hybrid on both signed draws under every observer. Combined worst slices were hybrid 0.19645 vs all-IQ2_S 0.17363 on Smol, 0.21441 vs 0.21537 on Phi, and 0.21619 vs 0.23602 on OLMo. Cross-observer worst-case selection therefore favors the hybrid; all-IQ2_S also showed a confident Smol English/deep regression on signed round 2.
Integrity and exact runtime checks
- GGUF SHA-256:
d1494860fec450cbe61ee26ae17eba7723ee498c08001d88410e1aafe9bca8da - Manifest SHA-256:
9223ffb462371deafcbac08b922eca7146f9e549c627a5c2f1f37d41096581e6 - Ralph content hash:
9d71cedb7443b378c1619883f323c68e099812885c448c82a68341ae3ccd522d - Declared compute: 4.3 H100-hours
The exact two-file package passes Ralph v2 inspect_checkpoint, the sub2 code/container
bit gate, and pinned-parent compatibility. It test-loads and generates under
llama-cpp-python==0.3.34 with CUDA, n_ctx=8192, n_batch=512, and all layers offloaded.
On each of two signed post-commit selections, three independent runs (one per observer)
produced byte-identical hashes for all 72 generated steps.
Held-out observer diagnostics
The fast immutable baseline and this candidate were scored on the same two actual signed post-commit selections (144 samples per observer). Frozen parent steps came from signature-verified public records. When an observer matched a signed record, the published continuation was reused exactly; otherwise a new continuation was generated. All distributions and candidate effects were recomputed locally.
| Observer | Fast worst slice | Championship worst slice | Raw delta | Paired 95% LCB |
|---|---|---|---|---|
| SmolLM2-1.7B-Instruct | 0.17706 | 0.19645 | +0.01939 | +0.00256 |
| Phi-3-mini-4k-instruct | 0.21511 | 0.21441 | -0.00070 | -0.01444 |
| OLMo-2-1124-7B-Instruct | 0.21434 | 0.21619 | +0.00185 | -0.01981 |
No observer showed a statistically confident regression axis. The candidate was materially better on Smol and essentially tied across the two draws on Phi and OLMo. These paired lower bounds do not clear Ralph's +0.05 contested-crown margin, so they must not be interpreted as a robust dethrone claim.
The candidate passed the formal repetition gate on both signed selections: 18/72 and 14/72 outputs exceeded the six-gram loop threshold. The fast baseline failed the first selection at 22/72 (30.56%) and passed the second at 21/72 (29.17%). Every observer run scored 72/72 shared samples with zero discards and no scorer reasons.
These are unsigned miner-side held-out diagnostics, not validator verdicts or crown
certificates. At the last pre-upload status refresh, the sub2 lane had no commitment and no
incumbent crown.
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We're not able to determine the quantization variants.