Instructions to use Nanthasit/sakthai-coder-1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Nanthasit/sakthai-coder-1.5b 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 Nanthasit/sakthai-coder-1.5b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Nanthasit/sakthai-coder-1.5b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Nanthasit/sakthai-coder-1.5b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Nanthasit/sakthai-coder-1.5b: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 Nanthasit/sakthai-coder-1.5b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Nanthasit/sakthai-coder-1.5b: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 Nanthasit/sakthai-coder-1.5b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Nanthasit/sakthai-coder-1.5b:Q4_K_M

Use Docker

docker model run hf.co/Nanthasit/sakthai-coder-1.5b:Q4_K_M

LM Studio
Jan

vLLM

How to use Nanthasit/sakthai-coder-1.5b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nanthasit/sakthai-coder-1.5b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nanthasit/sakthai-coder-1.5b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nanthasit/sakthai-coder-1.5b:Q4_K_M

Ollama
How to use Nanthasit/sakthai-coder-1.5b with Ollama:
```
ollama run hf.co/Nanthasit/sakthai-coder-1.5b:Q4_K_M
```

Unsloth Studio

How to use Nanthasit/sakthai-coder-1.5b 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 Nanthasit/sakthai-coder-1.5b 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 Nanthasit/sakthai-coder-1.5b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Nanthasit/sakthai-coder-1.5b to start chatting

How to use Nanthasit/sakthai-coder-1.5b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Nanthasit/sakthai-coder-1.5b:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Nanthasit/sakthai-coder-1.5b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Nanthasit/sakthai-coder-1.5b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Nanthasit/sakthai-coder-1.5b: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 Nanthasit/sakthai-coder-1.5b:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Nanthasit/sakthai-coder-1.5b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Nanthasit/sakthai-coder-1.5b: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 "Nanthasit/sakthai-coder-1.5b: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"

Docker Model Runner
How to use Nanthasit/sakthai-coder-1.5b with Docker Model Runner:
```
docker model run hf.co/Nanthasit/sakthai-coder-1.5b:Q4_K_M
```

Lemonade

How to use Nanthasit/sakthai-coder-1.5b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Nanthasit/sakthai-coder-1.5b:Q4_K_M

Run and chat with the model

lemonade run user.sakthai-coder-1.5b-Q4_K_M

List all available models

lemonade list

sakthai-coder-1.5b

File size: 1,266 Bytes

64a6fc8

{
  "benchmark": "verified-trust-pass",
  "date": "2026-07-25",
  "verifier": "SakThai Agent",
  "engine": "llama.cpp Q4_K_M, 2 cores, 2 threads, temp=0.1",
  "methodology": "Each test run 1x with timeout=20s. Same prompt format across all models. Results verified by SakThai.",
  "results": {
    "tool_calling": {
      "1.5B": {"get_weather": "pass", "search_web": "pass", "calculate": "pass", "get_time": "pass", "irrelevance": "pass", "score": "5/5", "verified": true},
      "0.5B": {"get_weather": "pass", "search_web": "pass", "calculate": "pass", "get_time": "pass", "irrelevance": "fail", "score": "4/5", "verified": true}
    },
    "coding": {
      "Coder 1.5B": {"factorial": "pass", "debugging": "pass", "async_explain": "pass", "refactor": "pass", "primes": "pass", "score": "5/5", "verified": true}
    },
    "speed": {
      "0.5B": {"size": "380 MB", "time": "2s", "speed": "24 tok/s", "verified": true},
      "1.5B": {"size": "934 MB", "time": "5s", "speed": "9 tok/s", "verified": true},
      "Coder": {"size": "1.1 GB", "time": "6s", "speed": "9 tok/s", "verified": true}
    }
  },
  "previous_results": {
    "1.5B_bfcl_v1": "4/5",
    "1.5B_bfcl_v2_verified": "5/5",
    "improvement": "+1 score (now handles search_web correctly)"
  }
}