Instructions to use tchbcb/samai-8b-M8 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 tchbcb/samai-8b-M8 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 tchbcb/samai-8b-M8:Q4_K_M # Run inference directly in the terminal: llama cli -hf tchbcb/samai-8b-M8:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tchbcb/samai-8b-M8:Q4_K_M # Run inference directly in the terminal: llama cli -hf tchbcb/samai-8b-M8:Q4_K_M
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 tchbcb/samai-8b-M8:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf tchbcb/samai-8b-M8:Q4_K_M
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 tchbcb/samai-8b-M8:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf tchbcb/samai-8b-M8:Q4_K_M
Use Docker
docker model run hf.co/tchbcb/samai-8b-M8:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use tchbcb/samai-8b-M8 with Ollama:
ollama run hf.co/tchbcb/samai-8b-M8:Q4_K_M
- Unsloth Desktop
- Pi
How to use tchbcb/samai-8b-M8 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tchbcb/samai-8b-M8:Q4_K_M
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": "tchbcb/samai-8b-M8:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use tchbcb/samai-8b-M8 with Docker Model Runner:
docker model run hf.co/tchbcb/samai-8b-M8:Q4_K_M
- Lemonade
How to use tchbcb/samai-8b-M8 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tchbcb/samai-8b-M8:Q4_K_M
Run and chat with the model
lemonade run user.samai-8b-M8-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use tchbcb/samai-8b-M8 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tchbcb/samai-8b-M8:Q4_K_M
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 tchbcb/samai-8b-M8:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use tchbcb/samai-8b-M8 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tchbcb/samai-8b-M8:Q4_K_M
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 "tchbcb/samai-8b-M8:Q4_K_M" \ --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"
R12 ops archive: r12_c2_deploy.py
Browse files
artifacts/m15_scripts/r12_c2_deploy.py
ADDED
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import os, json
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def sh(c, t=120):
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import subprocess
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try:
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p = subprocess.run(c, shell=True, capture_output=True, text=True, timeout=t, errors="replace")
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return ((p.stdout or "") + (p.stderr or "")).strip()
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except Exception as e:
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return "ERR:" + repr(e)[:80]
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out = {}
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code = '''
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from huggingface_hub import hf_hub_download
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import shutil
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tok = open("/root/.cache/huggingface/token").read().strip()
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p = hf_hub_download("tchbcb/samai-8b-M8", "artifacts/m15_scripts/r12_deploy_r10.py", token=tok)
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shutil.copy(p, "/tmp/k8b/r12_deploy_r10.py")
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print("OK", __import__("os").path.getsize("/tmp/k8b/r12_deploy_r10.py"))
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'''
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open("/tmp/k8b/r12_hfget2.py", "w").write(code)
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out["hfget"] = sh("python3 /tmp/k8b/r12_hfget2.py", 120)
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# 鏍¢獙 adapter 鍦ㄤ綅鍚庣偣鐏儴缃查摼
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out["adapter"] = sh("du -sh /tmp/k8b/m12_adapters/m13_r10 2>/dev/null | cut -f1; ls /tmp/k8b/m12_adapters/m13_r10 | head -4")
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out["kick"] = sh("cd /tmp/k8b && rm -f DEPLOY_R10_FAIL && setsid nohup python3 r12_deploy_r10.py > /dev/null 2>&1 & sleep 2; ps ax | grep '[r]12_deploy_r10' | head -1 | cut -c1-90", 20)
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out["log"] = sh("sleep 8; tail -3 /tmp/k8b/r12_deploy.log 2>/dev/null | cut -c1-130")
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print(json.dumps(out, ensure_ascii=False))
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