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 Desktop

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 @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": "Nanthasit/sakthai-coder-1.5b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Hermes Agent

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

OpenClaw

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"

sakthai-coder-1.5b / .venv /bin /activate.ps1

Nanthasit

Remove accidentally uploaded coder-readme.md

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	# Copyright (c) 2020-202x The virtualenv developers
	#
	# Permission is hereby granted, free of charge, to any person obtaining
	# a copy of this software and associated documentation files (the
	# "Software"), to deal in the Software without restriction, including
	# without limitation the rights to use, copy, modify, merge, publish,
	# distribute, sublicense, and/or sell copies of the Software, and to
	# permit persons to whom the Software is furnished to do so, subject to
	# the following conditions:
	#
	# The above copyright notice and this permission notice shall be
	# included in all copies or substantial portions of the Software.
	#
	# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
	# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
	# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
	# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
	# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
	# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
	# WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

	$script:THIS_PATH = $myinvocation.mycommand.path
	$script:BASE_DIR = Split-Path (Resolve-Path "$THIS_PATH/..") -Parent

	function global:deactivate([switch] $NonDestructive) {
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	$env:PATH = $variable:_OLD_VIRTUAL_PATH
	Remove-Variable "_OLD_VIRTUAL_PATH" -Scope global
	}

	if (Test-Path function:_old_virtual_prompt) {
	$function:prompt = $function:_old_virtual_prompt
	Remove-Item function:\_old_virtual_prompt
	}

	if ($env:VIRTUAL_ENV) {
	Remove-Item env:VIRTUAL_ENV -ErrorAction SilentlyContinue
	}

	if ($env:VIRTUAL_ENV_PROMPT) {
	Remove-Item env:VIRTUAL_ENV_PROMPT -ErrorAction SilentlyContinue
	}

	if (!$NonDestructive) {
	# Self destruct!
	Remove-Item function:deactivate
	Remove-Item function:pydoc
	}
	}

	function global:pydoc {
	python -m pydoc $args
	}

	# unset irrelevant variables
	deactivate -nondestructive

	$VIRTUAL_ENV = $BASE_DIR
	$env:VIRTUAL_ENV = $VIRTUAL_ENV

	if ("sakthai-agent" -ne "") {
	$env:VIRTUAL_ENV_PROMPT = "sakthai-agent"
	}
	else {
	$env:VIRTUAL_ENV_PROMPT = $( Split-Path $env:VIRTUAL_ENV -Leaf )
	}

	New-Variable -Scope global -Name _OLD_VIRTUAL_PATH -Value $env:PATH

	$env:PATH = "$env:VIRTUAL_ENV/bin:" + $env:PATH
	if (!$env:VIRTUAL_ENV_DISABLE_PROMPT) {
	function global:_old_virtual_prompt {
	""
	}
	$function:_old_virtual_prompt = $function:prompt

	function global:prompt {
	# Add the custom prefix to the existing prompt
	$previous_prompt_value = & $function:_old_virtual_prompt
	("(" + $env:VIRTUAL_ENV_PROMPT + ") " + $previous_prompt_value)
	}
	}