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 30

Commit

bc8ec27

verified ·

1 Parent(s): f9bb9cf

Upload .eval_results/health-check-sakthai-coder-1.5b-2026-07-30-3.yaml with huggingface_hub

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.eval_results/health-check-sakthai-coder-1.5b-2026-07-30-3.yaml +175 -0

.eval_results/health-check-sakthai-coder-1.5b-2026-07-30-3.yaml ADDED Viewed

	@@ -0,0 +1,175 @@

+target_model:
+  id: Nanthasit/sakthai-coder-1.5b
+  slug: sakthai-coder-1.5b
+eval_metadata:
+  schema: llm_cron
+  generated_at: '2026-07-30T22:46:05Z'
+  generated_by: sakthai-agent-cron
+  model_type: text-generation
+popularity:
+  downloads: 93
+  likes: 0
+  max_sibling_downloads: 1599
+  dl_score: 6
+  likes_score: 0
+  score: 4
+  weight: 0.20
+momentum:
+  age_days: 6.52
+  velocity: 14.27
+  max_sibling_velocity: 63.95
+  velocity_rank: 9
+  ratio_score: 22
+  rank_score: 20
+  score: 21
+  weight: 0.20
+benchmarks:
+  model_index_present: true
+  metric_count: 4
+  all_verified: false
+  score: 60
+  weight: 0.25
+  entries:
+    - dataset: HumanEval
+      dataset_type: openai_humaneval
+      metric: 'pass@1 (base model reference)'
+      metric_type: 'pass@1'
+      value: 74.40
+      verified: false
+    - dataset: MBPP
+      dataset_type: mbpp
+      metric: 'pass@1 (base model reference)'
+      metric_type: 'pass@1'
+      value: 71.20
+      verified: false
+    - dataset: MultiPL-E (Python)
+      dataset_type: multipl_e
+      metric: 'pass@1 (base model reference)'
+      metric_type: 'pass@1'
+      value: 65.30
+      verified: false
+    - dataset: SakThai Coding Suite (internal)
+      dataset_type: custom
+      metric: 'pass@1 (fine-tuned model, internal)'
+      metric_type: 'pass@1'
+      value: 100
+      verified: false
+card_quality:
+  license: apache-2.0
+  base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
+  tags_count: 10
+  datasets_count: 3
+  readme_bytes: 15490
+  score: 100
+  weight: 0.20
+repo_summary:
+  total_siblings: 1558
+  total_storage_bytes: 1218622744
+  total_gb: 1.13
+  weight_files:
+    - path: qwen2.5-coder-1.5b-instruct-q4_k_m.gguf
+      size_bytes: 1117320768
+  has_weights: true
+  config_exists: false
+  dev_artifact_dirs:
+    - .hypothesis
+    - .pytest_cache
+    - .ruff_cache
+    - .venv
+  dev_artifact_count: 4
+hygiene:
+  score: 40
+  weight: 0.15
+  deductions:
+    dev_artifacts: 60
+    storage_bloat: 0
+sibling_comparison:
+  - id: Nanthasit/sakthai-context-1.5b-merged
+    downloads: 1599
+    likes: 0
+    pipeline_tag: text-generation
+  - id: Nanthasit/sakthai-context-0.5b-merged
+    downloads: 1370
+    likes: 0
+    pipeline_tag: text-generation
+  - id: Nanthasit/sakthai-context-7b-merged
+    downloads: 744
+    likes: 0
+    pipeline_tag: text-generation
+  - id: Nanthasit/sakthai-context-7b-128k
+    downloads: 506
+    likes: 0
+    pipeline_tag: text-generation
+  - id: Nanthasit/sakthai-context-7b-tools
+    downloads: 399
+    likes: 0
+    pipeline_tag: text-generation
+  - id: Nanthasit/sakthai-embedding-multilingual
+    downloads: 362
+    likes: 0
+    pipeline_tag: sentence-similarity
+  - id: Nanthasit/sakthai-context-1.5b-tools
+    downloads: 349
+    likes: 0
+    pipeline_tag: text-generation
+  - id: Nanthasit/sakthai-vision-7b
+    downloads: 186
+    likes: 1
+    pipeline_tag: image-to-text
+  - id: Nanthasit/sakthai-tts-model
+    downloads: 150
+    likes: 0
+    pipeline_tag: text-to-speech
+  - id: Nanthasit/sakthai-context-0.5b-tools
+    downloads: 94
+    likes: 0
+    pipeline_tag: text-generation
+  - id: Nanthasit/sakthai-embedding
+    downloads: 23
+    likes: 0
+    pipeline_tag: sentence-similarity
+  - id: Nanthasit/sakthai-context-1.5b-tools-v2
+    downloads: 0
+    likes: 0
+    pipeline_tag: text-generation
+  - id: Nanthasit/sakthai-context-1.5b-merged-v2
+    downloads: 0
+    likes: 0
+    pipeline_tag: text-generation
+  - id: Nanthasit/sakthai-plus-1.5b
+    downloads: 0
+    likes: 0
+    pipeline_tag: text-generation
+  - id: Nanthasit/sakthai-plus-1.5b-lora
+    downloads: 0
+    likes: 0
+    pipeline_tag: text-generation
+  - id: Nanthasit/sakthai-plus-1.5b-coder
+    downloads: 0
+    likes: 0
+    pipeline_tag: text-generation
+  - id: Nanthasit/sakthai-coder-browser-lora
+    downloads: 0
+    likes: 0
+    pipeline_tag: null
+  - id: Nanthasit/sakthai-coder-browser
+    downloads: 0
+    likes: 0
+    pipeline_tag: text-generation
+health_score:
+  overall: 46
+  popularity: 4
+  momentum: 21
+  benchmarks: 60
+  card_quality: 100
+  hygiene: 40
+  weighting: 'pop=20% mom=20% bench=25% card=20% hyg=15%'
+delta:
+  score_change: 0
+  downloads_change: 0
+  likes_change: 0
+  from_prior: health-check-sakthai-coder-1.5b-2026-07-30-2.yaml
+assessment:
+  status: stable
+  notes: No change in downloads/likes since previous check 5 minutes ago. Score unchanged at 46/100. GGUF-only coder model (1.04 GB, Q4_K_M, qwen2 arch, 32K context). Card complete with license/base_model/10 tags/3 datasets. 4 unverified benchmarks listed. 4 dev artifact directories penalizing hygiene score.
+  recommendation: Add verified benchmark results to model-index. Clean .venv, .pytest_cache, .ruff_cache, and .hypothesis directories from repo to improve hygiene score (could gain 15 points).