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 Jul 25

Commit

b13b119

verified ·

1 Parent(s): 1e1c62c

Add family cross-link table + conversational tag

Browse files

Files changed (1) hide show

README.md +17 -0

README.md CHANGED Viewed

@@ -5,6 +5,7 @@ language:
 library_name: transformers
 pipeline_tag: text-generation
 tags:
 - qwen2.5
 - coder
 - code
@@ -36,3 +37,19 @@ Code-optimized model converted to GGUF Q4_K_M for CPU inference. Based on Qwen2.
 - [1.5B SakThai (general)](https://huggingface.co/Nanthasit/sakthai-context-1.5b-merged)
 - [0.5B SakThai (lightweight)](https://huggingface.co/Nanthasit/sakthai-context-0.5b-merged)
 - [1.5B Coder (this one)](https://huggingface.co/Nanthasit/sakthai-coder-1.5b)

 library_name: transformers
 pipeline_tag: text-generation
 tags:
+- conversational
 - qwen2.5
 - coder
 - code
 - [1.5B SakThai (general)](https://huggingface.co/Nanthasit/sakthai-context-1.5b-merged)
 - [0.5B SakThai (lightweight)](https://huggingface.co/Nanthasit/sakthai-context-0.5b-merged)
 - [1.5B Coder (this one)](https://huggingface.co/Nanthasit/sakthai-coder-1.5b)
+## SakThai Model Family
+| Model | Size | Type | Downloads |
+|-------|:----:|:----:|:---------:|
+| [1.5B-merged](https://huggingface.co/Nanthasit/sakthai-context-1.5b-merged) | 934 MB | Tool-calling GGUF | 942 |
+| [0.5B-merged](https://huggingface.co/Nanthasit/sakthai-context-0.5b-merged) | 380 MB | Lightweight GGUF | 785 |
+| [7B-merged](https://huggingface.co/Nanthasit/sakthai-context-7b-merged) | 15 GB | Full-size model | 534 |
+| [7B-128K](https://huggingface.co/Nanthasit/sakthai-context-7b-128k) | 15 GB | Extended context | 324 |
+| [Coder-1.5B](https://huggingface.co/Nanthasit/sakthai-coder-1.5b) | 1.1 GB | Code GGUF | 15 |
+| [Embedding](https://huggingface.co/Nanthasit/sakthai-embedding) | 80 MB | Search | 28 |
+| [Vision-7B](https://huggingface.co/Nanthasit/sakthai-vision-7b) | 3.9 GB | Multimodal GGUF | 0 |
+| [TTS-Model](https://huggingface.co/Nanthasit/sakthai-tts-model) | 141 MB | Speech GGUF | 0 |
+| [Multilingual Embedding](https://huggingface.co/Nanthasit/sakthai-embedding-multilingual) | 80 MB | 50+ languages | 0 |
+[Full collection →](https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02)