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

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

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"

samai-8b-M8

File size: 1,112 Bytes

4f7460f

#!/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()