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"

Nanthasit commited on Aug 1

Commit

316ae0a

verified ·

1 Parent(s): 6d36dd9

Improve model card: add model-size badge, refresh family-table downloads, update coverage wording to 26 public models.

Browse files

Files changed (1) hide show

README.md +2 -1

README.md CHANGED Viewed

@@ -89,6 +89,7 @@ model-index:
   <img src="https://img.shields.io/badge/dynamic/json?url=https%3A//huggingface.co/api/models/Nanthasit/sakthai-coder-1.5b&query=%24.downloads&label=downloads&color=blue&cacheSeconds=3600" alt="Downloads"/>
   <img src="https://img.shields.io/badge/license-Apache%202.0-green" alt="License"/>
   <img src="https://img.shields.io/badge/GGUF-Q4__K__M%201.12GB-orange" alt="GGUF"/>
   <a href="https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02"><img src="https://img.shields.io/badge/-SakThai%20Family-6644cc" alt="Collection"/></a>
 </p>
@@ -399,7 +400,7 @@ All 26 public models (downloads live, sizes verified via HF API on 2026-07-31
 | [vision-7b](https://huggingface.co/Nanthasit/sakthai-vision-7b) | 4.1 GB | Image to text (LLaVA GGUF) | 186 |
 | [tts-model](https://huggingface.co/Nanthasit/sakthai-tts-model) | 141 MB | Text-to-speech, 15 langs | 150 |
 | [context-0.5b-tools](https://huggingface.co/Nanthasit/sakthai-context-0.5b-tools) | 988 MB | Ultra-light tool-calling | 94 |
-| **coder-1.5b (you are here)** | **1.12 GB** | **Code generation + tool-calling** | **93** |
 | [context-1.5b-tools-v2](https://huggingface.co/Nanthasit/sakthai-context-1.5b-tools-v2) | LoRA 74 MB | 🆕 v2 tool-calling adapter | 0 |
 | [context-1.5b-merged-v2](https://huggingface.co/Nanthasit/sakthai-context-1.5b-merged-v2) | 3.1 GB | 🆕 v2 merged | 0 |
 | [plus-1.5b](https://huggingface.co/Nanthasit/sakthai-plus-1.5b) | 3.1 GB | 🆕 Plus merged | 0 |

   <img src="https://img.shields.io/badge/dynamic/json?url=https%3A//huggingface.co/api/models/Nanthasit/sakthai-coder-1.5b&query=%24.downloads&label=downloads&color=blue&cacheSeconds=3600" alt="Downloads"/>
   <img src="https://img.shields.io/badge/license-Apache%202.0-green" alt="License"/>
   <img src="https://img.shields.io/badge/GGUF-Q4__K__M%201.12GB-orange" alt="GGUF"/>
+  <img src="https://img.shields.io/badge/model--size-1.12GB-blue" alt="Model size"/>
   <a href="https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02"><img src="https://img.shields.io/badge/-SakThai%20Family-6644cc" alt="Collection"/></a>
 </p>
 | [vision-7b](https://huggingface.co/Nanthasit/sakthai-vision-7b) | 4.1 GB | Image to text (LLaVA GGUF) | 186 |
 | [tts-model](https://huggingface.co/Nanthasit/sakthai-tts-model) | 141 MB | Text-to-speech, 15 langs | 150 |
 | [context-0.5b-tools](https://huggingface.co/Nanthasit/sakthai-context-0.5b-tools) | 988 MB | Ultra-light tool-calling | 94 |
+| **coder-1.5b (you are here)** | **1.12 GB** | **Code generation + tool-calling** | **151** |
 | [context-1.5b-tools-v2](https://huggingface.co/Nanthasit/sakthai-context-1.5b-tools-v2) | LoRA 74 MB | 🆕 v2 tool-calling adapter | 0 |
 | [context-1.5b-merged-v2](https://huggingface.co/Nanthasit/sakthai-context-1.5b-merged-v2) | 3.1 GB | 🆕 v2 merged | 0 |
 | [plus-1.5b](https://huggingface.co/Nanthasit/sakthai-plus-1.5b) | 3.1 GB | 🆕 Plus merged | 0 |