Text Classification
GGUF
ollaya
ollama
trainaya
decision-model
system-1
llm-logits-v1
calibrated
ministral
conversational
Instructions to use solarpush/push-3b 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 solarpush/push-3b 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 solarpush/push-3b # Run inference directly in the terminal: llama cli -hf solarpush/push-3b
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf solarpush/push-3b # Run inference directly in the terminal: llama cli -hf solarpush/push-3b
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 solarpush/push-3b # Run inference directly in the terminal: ./llama-cli -hf solarpush/push-3b
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 solarpush/push-3b # Run inference directly in the terminal: ./build/bin/llama-cli -hf solarpush/push-3b
Use Docker
docker model run hf.co/solarpush/push-3b
- LM Studio
- Jan
- Ollama
How to use solarpush/push-3b with Ollama:
ollama run hf.co/solarpush/push-3b
- Unsloth Desktop
- Pi
How to use solarpush/push-3b with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf solarpush/push-3b
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": "solarpush/push-3b" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use solarpush/push-3b with Docker Model Runner:
docker model run hf.co/solarpush/push-3b
- Lemonade
How to use solarpush/push-3b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull solarpush/push-3b
Run and chat with the model
lemonade run user.push-3b-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use solarpush/push-3b with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf solarpush/push-3b
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 solarpush/push-3b
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use solarpush/push-3b with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf solarpush/push-3b
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 "solarpush/push-3b" \ --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"
Download calibration.json from solarpush/push-3b: direct link, hf CLI and curl.
- Browser
- Download file 788 Bytes
-
https://huggingface.co/solarpush/push-3b/resolve/main/calibration.json
- Command line
-
hf download hf://solarpush/push-3b/calibration.json
-
curl -L -o calibration.json https://huggingface.co/solarpush/push-3b/resolve/main/calibration.json
788 Bytes
| { | |
| "temperature": [ | |
| 1.843, | |
| 2.1509, | |
| 1.0447 | |
| ], | |
| "temperature_by_options": { | |
| "noul:2": 1.0447, | |
| "score:3-5": 2.1509, | |
| "choice:2": 1.843, | |
| "choice:3-5": 1.8658, | |
| "choice:6-10": 1.7508, | |
| "choice:11+": 1.5465 | |
| }, | |
| "temperature_range": [ | |
| 0.25, | |
| 16.0 | |
| ], | |
| "baseline_accuracy": { | |
| "total": 0.6289, | |
| "choice": 0.5714, | |
| "score": 0.4099, | |
| "noul": 0.775 | |
| }, | |
| "oof_accuracy": { | |
| "total": 0.683, | |
| "choice": 0.6446, | |
| "score": 0.516, | |
| "noul": 0.7876 | |
| }, | |
| "post_quant_accuracy": { | |
| "total": 0.683, | |
| "choice": 0.6446, | |
| "score": 0.516, | |
| "noul": 0.7876 | |
| }, | |
| "trained_samples": 3050, | |
| "source": "Trainaya Multi-Stage GGUF Decision Trainer (55/45 Kernel Ridge + Masked Softmax KL+Brier+RPS AdamW, 5-Fold OOF Calibrated)" | |
| } |