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"

YAML Metadata Error:Invalid Eval Result format in .eval_results/health-check.yaml

Check out the documentation for more information.

Show details

✖ Invalid input: expected array, received object

sakthai-coder-1.5b / .eval_results /health-check.yaml

Nanthasit

health-eval: automated health check 2026-07-30

7cfc347 verified 2 months ago

Raw History Blame

3.34 kB

	# Health Check Report
	# Generated: 2026-07-30T23:06 UTC
	# Model: Nanthasit/sakthai-coder-1.5b
	# Tool: sakthai-agent health-eval (cron)

	model: Nanthasit/sakthai-coder-1.5b
	eval_timestamp: 2026-07-30T23:06:00Z

	metrics:
	popularity:
	downloads: 93
	likes: 0
	last_modified: 2026-07-30T22:56:24.000Z
	days_since_last_update: 0
	metadata:
	pipeline_tag: text-generation
	library_name: transformers
	license: apache-2.0
	base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
	private: false
	tags:
	- text-generation
	- transformers
	- qwen
	- coder
	- sakthai
	- finetuned
	- tool-calling
	- function-calling
	- 1.5b
	- llama.cpp
	datasets:
	- Nanthasit/sakthai-combined-v6
	- Nanthasit/sakthai-combined-v7
	- Nanthasit/sakthai-irrelevance-supplement
	datasets_count: 3

	files:
	total_siblings: 1563
	model_weight_files: 1
	model_weights:
	- file: qwen2.5-coder-1.5b-instruct-q4_k_m.gguf
	size_bytes: 1117320768
	size_gb: 1.041
	total_model_size_gb: 1.041
	contamination: true
	contamination_note: "1563 siblings detected. Repo contains a full .venv/ directory (1254 .py files, pip packages, .dist-info). Only 1 actual model weight file. Recommends cleanup: remove .venv/ from repo."

	benchmarks:
	model_index_present: true
	results:
	- dataset: HumanEval
	task: text-generation
	metric: pass@1 (base model reference)
	value: 74.4
	verified: false
	- dataset: MBPP
	task: text-generation
	metric: pass@1 (base model reference)
	value: 71.2
	verified: false
	- dataset: MultiPL-E (Python)
	task: text-generation
	metric: pass@1 (base model reference)
	value: 65.3
	verified: false
	- dataset: SakThai Coding Suite (internal)
	task: text-generation
	metric: pass@1 (fine-tuned model, internal)
	value: 100.0
	verified: false
	source: internal-local-llama-cpp-2026-07-25
	benchmark_quality:
	- "Internal benchmark (SakThai Coding Suite) shows 100% pass@1 but unverified — likely overfit or too narrow"
	- "Base model reference scores from Qwen2.5-Coder-1.5B-Instruct used as comparison; no verified fine-tuned scores"
	- "No multi-trial methodology reported; single-trial results may be unreliable"

	health_score:
	overall: 6.5
	max: 10
	factors:
	popularity_downloads: 2
	likes: 0
	metadata_quality: 8
	model_index: 7
	file_hygiene: 3
	recency: 9
	concerns:
	- "Repo severely contaminated with .venv/ directory (1254 transient .py files)"
	- "0 likes — no community engagement yet"
	- "Benchmark scores unverified (marked verified: false)"
	- "Only 1 model weight format (GGUF Q4_K_M); no safetensors variant"
	recommendations:
	- action: REMOVE_VENV
	priority: high
	detail: "Delete .venv/ from repo — it adds 1254+ useless files and obscures real model content"
	- action: ADD_SAFETENSORS
	priority: medium
	detail: "Consider adding a safetensors version for Transformers-native loading"
	- action: VERIFY_BENCHMARKS
	priority: medium
	detail: "Run multi-trial benchmarks and mark as verified"
	- action: ADD_MODEL_CARD_SECTIONS
	priority: low
	detail: "Add usage example, recommended prompt format, and hardware requirements"