Text Generation
GGUF
English
code
coder
qwen2.5
qwen2.5-coder
llama-cpp
llama.cpp
ollama
code-generation
tool-calling
conversational
cpu-inference
small-language-model
offline
sakthai
house-of-sak
Eval Results (legacy)
Eval Results
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
- llama.cpp
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
- Pi
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"
File size: 4,713 Bytes
bc8ec27 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 | 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).
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