- Libraries
- MLX
How to use HamoAI/hamo-score-0.6b with MLX:
# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# Generate text with mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("HamoAI/hamo-score-0.6b")
prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use HamoAI/hamo-score-0.6b 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 HamoAI/hamo-score-0.6b
# Run inference directly in the terminal:
llama cli -hf HamoAI/hamo-score-0.6b
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf HamoAI/hamo-score-0.6b
# Run inference directly in the terminal:
llama cli -hf HamoAI/hamo-score-0.6b
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 HamoAI/hamo-score-0.6b
# Run inference directly in the terminal:
./llama-cli -hf HamoAI/hamo-score-0.6b
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 HamoAI/hamo-score-0.6b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf HamoAI/hamo-score-0.6b
Use Docker
docker model run hf.co/HamoAI/hamo-score-0.6b
- LM Studio
- Jan
- vLLM
How to use HamoAI/hamo-score-0.6b with vLLM:
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HamoAI/hamo-score-0.6b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "HamoAI/hamo-score-0.6b",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'Use Docker
docker model run hf.co/HamoAI/hamo-score-0.6b
- Ollama
How to use HamoAI/hamo-score-0.6b with Ollama:
ollama run hf.co/HamoAI/hamo-score-0.6b
- Unsloth Studio
How to use HamoAI/hamo-score-0.6b with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for HamoAI/hamo-score-0.6b to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for HamoAI/hamo-score-0.6b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for HamoAI/hamo-score-0.6b to start chatting
- Pi
How to use HamoAI/hamo-score-0.6b with Pi:
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "HamoAI/hamo-score-0.6b"
Configure the model in Pi
# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
"providers": {
"mlx-lm": {
"baseUrl": "http://localhost:8080/v1",
"api": "openai-completions",
"apiKey": "none",
"models": [
{
"id": "HamoAI/hamo-score-0.6b"
}
]
}
}
}Run Pi
# Start Pi in your project directory:
pi
- OpenClaw new
How to use HamoAI/hamo-score-0.6b with OpenClaw:
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "HamoAI/hamo-score-0.6b"
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 "HamoAI/hamo-score-0.6b" \
--custom-provider-id mlx-lm \
--custom-compatibility openai \
--custom-text-input \
--accept-risk \
--skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- MLX LM
How to use HamoAI/hamo-score-0.6b with MLX LM:
Generate or start a chat session
# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "HamoAI/hamo-score-0.6b"
Run an OpenAI-compatible server
# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "HamoAI/hamo-score-0.6b"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "HamoAI/hamo-score-0.6b",
"messages": [
{"role": "user", "content": "Hello"}
]
}' - Docker Model Runner
How to use HamoAI/hamo-score-0.6b with Docker Model Runner:
docker model run hf.co/HamoAI/hamo-score-0.6b
- Lemonade
How to use HamoAI/hamo-score-0.6b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull HamoAI/hamo-score-0.6b
Run and chat with the model
lemonade run user.hamo-score-0.6b-{{QUANT_TAG}}List all available models
lemonade list
- Hermes Agent
How to use HamoAI/hamo-score-0.6b with Hermes Agent:
Start the MLX server
# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "HamoAI/hamo-score-0.6b"
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 HamoAI/hamo-score-0.6b
Run Hermes
hermes
- Atomic Chat
