Instructions to use Nanthasit/sakthai-plus-1.5b-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nanthasit/sakthai-plus-1.5b-coder with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Nanthasit/sakthai-plus-1.5b-coder")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Nanthasit/sakthai-plus-1.5b-coder", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nanthasit/sakthai-plus-1.5b-coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nanthasit/sakthai-plus-1.5b-coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nanthasit/sakthai-plus-1.5b-coder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Nanthasit/sakthai-plus-1.5b-coder

SGLang

How to use Nanthasit/sakthai-plus-1.5b-coder with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Nanthasit/sakthai-plus-1.5b-coder" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nanthasit/sakthai-plus-1.5b-coder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Nanthasit/sakthai-plus-1.5b-coder" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nanthasit/sakthai-plus-1.5b-coder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Nanthasit/sakthai-plus-1.5b-coder with Docker Model Runner:
```
docker model run hf.co/Nanthasit/sakthai-plus-1.5b-coder
```

sakthai-plus-1.5b-coder / README.md

Nanthasit

docs: improve README front matter, status, usage snippets, benchmarks, limitations

89da98b verified about 7 hours ago

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metadata

license: apache-2.0
language:
  - en
library_name: transformers
pipeline_tag: text-generation
tags:
  - transformers
  - qwen2.5
  - qwen2.5-coder
  - code-generation
  - agent
  - tool-calling
  - function-calling
  - sakthai
  - house-of-sak
  - no-weights
  - skeleton
  - cpu-inference
  - llama.cpp
  - ollama
  - offline
  - eval-results
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
datasets:
  - Nanthasit/sakthai-combined-v7
inference:
  parameters:
    temperature: 0.2
    max_new_tokens: 1024
    top_p: 0.9
  widget:
    - text: >-
        Write a Python function that checks if a string is a palindrome,
        ignoring spaces and punctuation.
      output:
        text: |-
          ```python
          def is_palindrome(s: str) -> bool:
              """Check if a string is a palindrome, ignoring spaces, punctuation, and case."""
              import re
              cleaned = re.sub(r'[^a-zA-Z0-9]', '', s).lower()
              return cleaned == cleaned[::-1]
          ```
    - text: Sort an array of numbers in JavaScript
      output:
        text: |-
          ```javascript
          function bubbleSort(arr) {
            const a = arr.slice();
            for (let i = 0; i < a.length; i++) {
              for (let j = 0; j < a.length - i - 1; j++) {
                if (a[j] > a[j + 1]) {
                  const tmp = a[j];
                  a[j] = a[j + 1];
                  a[j + 1] = tmp;
                }
              }
            }
            return a;
          }
          ```
model-index:
  - name: SakThai Plus 1.5B Coder
    results:
      - task:
          type: text-generation
          name: Code Generation Reference
        dataset:
          type: mbpp
          name: MBPP
        metrics:
          - type: pass@1
            value: 71.2
            name: MBPP pass@1 (base model reference)
            verified: false
            date: 2026-07-31T00:00:00.000Z
            notes: >-
              Inherited from Qwen/Qwen2.5-Coder-1.5B-Instruct; not measured on
              this fine-tune yet.
      - task:
          type: text-generation
          name: Local Tool-Calling Smoke
        dataset:
          type: custom
          name: SakThai tool-call smoke
        metrics:
          - type: valid-json
            value: pending
            name: Valid JSON rate
            verified: false
            date: 2026-08-01T00:00:00.000Z
            notes: >-
              Weights not uploaded; benchmark will run after first artifact
              push.
      - task:
          type: text-generation
          name: Hosted Inference Check
        dataset:
          type: custom
          name: HF router probe
        metrics:
          - type: availability
            value: 0
            name: Router availability
            verified: true
            date: 2026-08-01T00:00:00.000Z
            notes: >-
              Router returns model_not_supported because no weights are present
              in the repo.
extra:
  sibling: Nanthasit/sakthai-coder-1.5b

Qwen2.5-Coder 1.5B variant for code + tool use · weights placeholder + workflow
Roadmap model in the SakThai family · SakThai Model Family

Model Description

SakThai Plus 1.5B Coder is the coder-focused member of the SakThai Plus family. It is based on Qwen/Qwen2.5-Coder-1.5B-Instruct and targets two workflows: code generation and tool-calling from code-oriented prompts. This repository currently holds the recipe, metadata, and eval artifacts; weights are not uploaded yet.

This card exists so the training pipeline, evaluation history, and downstream integration points have a stable, versioned entry in the HF Hub. Once weights are pushed, it will become directly loadable with transformers and serveable with llama.cpp / Ollama from the repo.

Why this repo matters in the family:

🧑‍💻 Code-first base: Qwen2.5-Coder-1.5B-Instruct is a strong small-code model; this slot preserves the SakThai tool-calling adaptations for code use cases.
🔧 Tool-calling discipline: trained with code/agent instruction mix from sakthai-combined-v7.
📦 GGUF-ready path: once weights exist, the intended artifact is q4_k_m for CPU inference.
🧪 Live eval history: .eval_results/ contains cron metadata snapshots and smoke probes from 2026-07-30 to 2026-08-01.

Status

Item	State
Weights	❌ Not uploaded yet
GGUF	❌ Not uploaded yet
Config / metadata	✅ Present
Eval history	✅ `.eval_results/` snapshots present
Hub inference	❌ Router returns `model_not_supported` until weights are pushed

If you need a working SakThai coder model today, use:

How to Use

This repository is a workflow placeholder until weights are uploaded. Below are the two intended paths once weights are present.

Option A — Load with transformers (future)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Nanthasit/sakthai-plus-1.5b-coder"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are SakThai-Coder, a helpful coding assistant."},
    {"role": "user", "content": "Write a Python function that checks if a string is a palindrome."},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt")
out = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(out[0], skip_special_tokens=True))

Option B — CPU / offline inference via GGUF (future)

# After weights/GGUF are pushed:
huggingface-cli download Nanthasit/sakthai-plus-1.5b-coder --include "*.gguf" --local-dir ./
llama-cli -m sakthai-plus-1.5b-coder-q4_k_m.gguf \
  --prompt "[INST] Write a Python function that checks if a string is a palindrome. [/INST]" \
  -n 256

Training Details

Detail	Value
Base model	`Qwen/Qwen2.5-Coder-1.5B-Instruct`
Dataset	Nanthasit/sakthai-combined-v7
Framework	Transformers + PEFT/LoRA-family recipe
License	Apache 2.0

Evaluation & Benchmarks

This repo does not yet have verified model-level scores because there are no weights to load. The closest available references:

Benchmark	Source	Status
MBPP pass@1	Base model `Qwen2.5-Coder-1.5B-Instruct`	Inherited; not measured on this fine-tune
HF router inference	`.eval_results/benchmark-20260731_192407.yaml`	`model_not_supported` until weights are uploaded
Code smoke	`.eval_results/inference-check-2026-07-31.yaml`	Not runnable in this environment; metadata snapshot only

When weights are pushed, rerun:

llama-bench sakthai-plus-1.5b-coder-q4_k_m.gguf

and add results to .eval_results/.

Limitations

No weights uploaded yet. This repo is currently a metadata / workflow placeholder.
No standalone benchmarks yet. Published numbers are inherited from the base model, not this SakThai fine-tune.
No hosted inference. HF inference providers cannot serve a repo without weights.
Code quality depends on prompt format. This slot is tuned for tool + code prompts; plain chat performance may differ from the base instruct checkpoint.
Small-model tradeoffs. At 1.5B parameters, complex multi-file reasoning and very long context may degrade.

SakThai Model Family

Model	Downloads	Role
Context 1.5B Merged	1,855	Flagship tool-calling
Context 0.5B Merged	1,730	Lightweight edge
Context 7B Merged	1,055	High-power reasoning
Plus 1.5B Coder ⬅	0 / planned	Code + tool placeholder
Coder 1.5B	173	Ready coder variant

View the whole family collection

The House of Sak 🏠

Built from a shelter in Cork, Ireland, with $0 budget and no paid GPUs. This repo is part of an open-source ecosystem where every artifact is meant to be usable, auditable, and reproducible.

"We are one family — and becoming more." — Beer (beer-sakthai)

Support

⭐ Leave a like when weights are available
🐛 Report issues on GitHub
🔄 Share with anyone building accessible coding agents
🍴 Fork and experiment — Apache 2.0

Citation

@misc{sakthai-plus-1.5b-coder,
  title  = {SakThai Plus 1.5B Coder},
  author = {Beer (beer-sakthai) and SakThai},
  year   = {2026},
  url    = {https://huggingface.co/Nanthasit/sakthai-plus-1.5b-coder},
  note   = {Apache 2.0; weights placeholder based on Qwen/Qwen2.5-Coder-1.5B-Instruct}
}

If you use the base architecture, also cite:

@misc{qwen2.5-coder-2024,
  title  = {Qwen2.5-Coder},
  author = {Qwen Team},
  year   = {2024},
  url    = {https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct}
}

License

Apache 2.0. Base model Qwen/Qwen2.5-Coder-1.5B-Instruct retains its original license.

Built from a shelter in Cork, Ireland. Built with love, tears, and zero budget — to the world.

HF repo metadata API-verified 2026-08-01T10:39Z.