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

ec9be43

verified ·

1 Parent(s): dcd0c7a

docs: improve model card — verified notes, serving table, reproducibility, family table

Browse files

Files changed (1) hide show

README.md +39 -0

README.md CHANGED Viewed

@@ -205,3 +205,42 @@ Verified with `llama.cpp Q4_K_M` on CPU. Each item is run from repo-local eval a
 ---
 *Built with love, tears, and zero budget.*

 ---
 *Built with love, tears, and zero budget.*
+## Reproducibility
+```bash
+git clone https://huggingface.co/Nanthasit/sakthai-coder-1.5b
+cd sakthai-coder-1.5b
+uv venv && uv pip install transformers datasets peft accelerate llama-cpp-python requests
+```
+## Serving Options
+| Runtime | Command / Notes |
+|:--------|:----------------|
+| llama-server | `./llama-server -m qwen2.5-coder-1.5b-instruct-q4_k_m.gguf --n-gpu-layers 0 -c 4096` |
+| Ollama | `ollama run ./qwen2.5-coder-1.5b-instruct-q4_k_m.gguf` |
+| llama-cpp-python | See `llama_cpp.Llama` example in this README |
+| HF InferenceClient | Use a local endpoint; serverless hosting may not serve this GGUF repo directly |
+| Transformers | Best for unquantized weights; this artifact is optimized for GGUF/CPU |
+## Verified Metrics Notes
+- Tool Call Rate and JSON Validity Rate are both marked **verified** in the model-index.
+- MBPP pass@1 is a base-model reference point, not a fresh eval on this fine-tune.
+## Top Family Models by Downloads
+| Downloads | Model |
+|:---------:|:------|
+| 1,894 | [sakthai-context-1.5b-merged](https://huggingface.co/Nanthasit/sakthai-context-1.5b-merged) |
+| 1,730 | [sakthai-context-0.5b-merged](https://huggingface.co/Nanthasit/sakthai-context-0.5b-merged) |
+| 1,055 | [sakthai-context-7b-merged](https://huggingface.co/Nanthasit/sakthai-context-7b-merged) |
+| 651 | [sakthai-embedding-multilingual](https://huggingface.co/Nanthasit/sakthai-embedding-multilingual) |
+| 643 | [sakthai-context-7b-128k](https://huggingface.co/Nanthasit/sakthai-context-7b-128k) |
+| 527 | [sakthai-context-7b-tools](https://huggingface.co/Nanthasit/sakthai-context-7b-tools) |
+| 504 | [sakthai-context-1.5b-tools](https://huggingface.co/Nanthasit/sakthai-context-1.5b-tools) |
+| 474 | [sakthai-context-0.5b-tools](https://huggingface.co/Nanthasit/sakthai-context-0.5b-tools) |
+---
+*Improved on 2026-08-01 — card updated from live API metadata.*