🎛️Fine-tuning LLMs
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Collection of fine-tuned LLMs. • 28 items • Updated • 1
How to use kingabzpro/Llama-3.2-3b-it-mental-health with Transformers:
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("kingabzpro/Llama-3.2-3b-it-mental-health", device_map="auto")How to use kingabzpro/Llama-3.2-3b-it-mental-health with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf kingabzpro/Llama-3.2-3b-it-mental-health:F16 # Run inference directly in the terminal: llama cli -hf kingabzpro/Llama-3.2-3b-it-mental-health:F16
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf kingabzpro/Llama-3.2-3b-it-mental-health:F16 # Run inference directly in the terminal: llama cli -hf kingabzpro/Llama-3.2-3b-it-mental-health:F16
# 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 kingabzpro/Llama-3.2-3b-it-mental-health:F16 # Run inference directly in the terminal: ./llama-cli -hf kingabzpro/Llama-3.2-3b-it-mental-health:F16
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 kingabzpro/Llama-3.2-3b-it-mental-health:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf kingabzpro/Llama-3.2-3b-it-mental-health:F16
docker model run hf.co/kingabzpro/Llama-3.2-3b-it-mental-health:F16
How to use kingabzpro/Llama-3.2-3b-it-mental-health with Ollama:
ollama run hf.co/kingabzpro/Llama-3.2-3b-it-mental-health:F16
How to use kingabzpro/Llama-3.2-3b-it-mental-health with Pi:
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf kingabzpro/Llama-3.2-3b-it-mental-health:F16
# 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": "kingabzpro/Llama-3.2-3b-it-mental-health:F16"
}
]
}
}
}# Start Pi in your project directory: pi
How to use kingabzpro/Llama-3.2-3b-it-mental-health with Docker Model Runner:
docker model run hf.co/kingabzpro/Llama-3.2-3b-it-mental-health:F16
How to use kingabzpro/Llama-3.2-3b-it-mental-health with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull kingabzpro/Llama-3.2-3b-it-mental-health:F16
lemonade run user.Llama-3.2-3b-it-mental-health-F16
lemonade list
How to use kingabzpro/Llama-3.2-3b-it-mental-health with Hermes Agent:
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf kingabzpro/Llama-3.2-3b-it-mental-health:F16
# 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 kingabzpro/Llama-3.2-3b-it-mental-health:F16
hermes
How to use kingabzpro/Llama-3.2-3b-it-mental-health with OpenClaw:
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf kingabzpro/Llama-3.2-3b-it-mental-health:F16
# 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 "kingabzpro/Llama-3.2-3b-it-mental-health:F16" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
openclaw agent --local --agent main --message "Hello from Hugging Face"
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
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