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"
Download artifacts/r13_scripts/patch_skip_single.py from tchbcb/samai-8b-M8: direct link, hf CLI and curl.
- Browser
- Download file 1.11 kB
-
https://huggingface.co/tchbcb/samai-8b-M8/resolve/main/artifacts/r13_scripts/patch_skip_single.py
- Command line
-
hf download hf://tchbcb/samai-8b-M8/artifacts/r13_scripts/patch_skip_single.py
-
curl -L -o patch_skip_single.py https://huggingface.co/tchbcb/samai-8b-M8/resolve/main/artifacts/r13_scripts/patch_skip_single.py
1.11 kB
| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| """patch_skip_single.py — S3 单候选张量跳过: token_embd/output 等固定 q8_0 档不进 GPU 误差计算 | |
| (151936x4096 级张量 W fp32=2.5G + 中间量 → OOM 根因; E_tab 仅报告用, DP 只搜多候选张量)""" | |
| SK_OLD = ''' cands = ["q8_0"] | |
| W = torch.from_numpy(np.ascontiguousarray(t.data)).to(dev).float()''' | |
| SK_NEW = ''' cands = ["q8_0"] | |
| if len(cands) == 1: | |
| n1 = int(np.prod(shape)) | |
| E_tab[base] = {c: 0.0 for c in cands} | |
| sizes_tab[base] = {c: block_bytes(n1, c) for c in ["q4_0", "q5_0", "q6_k", "q8_0"]} | |
| table[base] = cands | |
| continue | |
| W = torch.from_numpy(np.ascontiguousarray(t.data)).to(dev).float()''' | |
| def main(): | |
| path = "/tmp/k8b/r13_chain.py" | |
| src = open(path).read() | |
| cnt = src.count(SK_OLD) | |
| assert cnt == 1, "anchor not unique: count=%d" % cnt | |
| src = src.replace(SK_OLD, SK_NEW, 1) | |
| compile(src, path, "exec") | |
| open(path, "w").write(src) | |
| print("PATCHED", path) | |
| print("PATCH_OK single-cand skip") | |
| main() | |