Instructions to use Nanthasit/sakthai-context-0.5b-tools with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nanthasit/sakthai-context-0.5b-tools with Transformers:

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

pipe = pipeline("text-generation", model="Nanthasit/sakthai-context-0.5b-tools")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nanthasit/sakthai-context-0.5b-tools")
model = AutoModelForCausalLM.from_pretrained("Nanthasit/sakthai-context-0.5b-tools", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nanthasit/sakthai-context-0.5b-tools with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nanthasit/sakthai-context-0.5b-tools"
# 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-context-0.5b-tools",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nanthasit/sakthai-context-0.5b-tools

SGLang

How to use Nanthasit/sakthai-context-0.5b-tools 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-context-0.5b-tools" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nanthasit/sakthai-context-0.5b-tools",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-context-0.5b-tools" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nanthasit/sakthai-context-0.5b-tools",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nanthasit/sakthai-context-0.5b-tools with Docker Model Runner:
```
docker model run hf.co/Nanthasit/sakthai-context-0.5b-tools
```

Nanthasit commited on 1 day ago

Commit

1974aad

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1 Parent(s): 2e959b8

docs: add Model Description, Limitations, and Citation sections to README

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@@ -114,6 +114,23 @@ model-index:
 **SakThai Context 0.5B Tools** is the v4 release of the tool-calling agent — fine-tuned from Qwen2.5-0.5B-Instruct with prompt-masked SFT. It achieves **91.2% selection accuracy** (multiset-corrected scorer) on the SakThai Bench v2, a 2.3× improvement over the 40.2% baseline.
 ## Benchmark Results
 All scores on [SakThai Bench v2](https://huggingface.co/datasets/Nanthasit/sakthai-bench-v2) (500 rows, 4 categories, multi-turn + held-out tools).
@@ -335,6 +352,36 @@ These sibling assets have traction building but less visibility. A download or l
 | [🔊 TTS Showcase](https://huggingface.co/spaces/Nanthasit/sakthai-tts) | Text-to-speech in 15 languages |
 | [📊 Leaderboard](https://huggingface.co/spaces/Nanthasit/sakthai-leaderboard) | Benchmark tracker for all models |
 ## Links
 - [🏠 SakThai Model Family](https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02)

 **SakThai Context 0.5B Tools** is the v4 release of the tool-calling agent — fine-tuned from Qwen2.5-0.5B-Instruct with prompt-masked SFT. It achieves **91.2% selection accuracy** (multiset-corrected scorer) on the SakThai Bench v2, a 2.3× improvement over the 40.2% baseline.
+## Model Description
+**SakThai Context 0.5B Tools** is the v4 release of the SakThai tool-calling agent -- an ultra-lightweight fine-tune of [Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) (494M parameters) trained with **prompt-masked SFT** (completion-only loss) for structured tool-calling.
+| Attribute | Value |
+|-----------|-------|
+| Base model | Qwen/Qwen2.5-0.5B-Instruct |
+| Parameters | 494M |
+| Model size | ~990 MB bfloat16 (Q4: ~295 MB) |
+| Context length | 32,768 tokens (32K) |
+| Method | SFT with prompt-masked loss (completion-only) |
+| Precision | bfloat16 |
+| Output format | `<tool_call>{"name": ..., "arguments": {...}}</tool_call>` XML |
+| License | Apache 2.0 |
+It calls tools with **91.8% selection accuracy** on [SakThai Bench v2](https://huggingface.co/datasets/Nanthasit/sakthai-bench-v2) (500 rows, 4 categories, multi-turn + held-out tools) -- a **2.3x improvement** over the 40.2% baseline -- while running in roughly **1 GB of RAM**, on a Raspberry Pi, or any edge device. The biggest gain came from per-assistant-turn prompt/completion pairs with `completion_only_loss=True`, which stopped the model from spending its gradient reproducing tool schemas instead of learning when to call tools.
 ## Benchmark Results
 All scores on [SakThai Bench v2](https://huggingface.co/datasets/Nanthasit/sakthai-bench-v2) (500 rows, 4 categories, multi-turn + held-out tools).
 | [🔊 TTS Showcase](https://huggingface.co/spaces/Nanthasit/sakthai-tts) | Text-to-speech in 15 languages |
 | [📊 Leaderboard](https://huggingface.co/spaces/Nanthasit/sakthai-leaderboard) | Benchmark tracker for all models |
+## Limitations
+- **Small model, small ceiling.** With 494M parameters, complex multi-step reasoning, long code generation, and nuanced instruction-following are limited. This is a *tool-calling* specialist, not a general reasoning engine.
+- **Exact argument accuracy is still weak** (45.7% overall; 38.7% simple / 52.7% parallel). The model usually selects the right tool but often emits imperfect argument values -- validate arguments before acting on them in production.
+- **Single-trial internal benchmarks.** All scores come from an internal eval run (greedy, batch 16, max 200 new tokens) on SakThai Bench v2 -- reproducible via `eval_bench.py`, but not a third-party benchmark such as the Open LLM Leaderboard.
+- **English-only.** Trained on English data; other languages degrade quickly.
+- **Tool-schema sensitivity.** The model emits `<tool_call>` XML. Non-standard schemas, other function-calling formats, or very long tool lists can reduce accuracy.
+- **32K context on a 0.5B model** does not mean 32K of reliable deep recall -- attention quality degrades well before the window fills for long documents.
+## Citation
+If you use this model, please cite the base model and the SakThai family work:
+```bibtex
+@misc{qwen25,
+  title={Qwen2.5 Technical Report},
+  author={Qwen Team, Alibaba Group},
+  year={2025},
+  url={https://arxiv.org/abs/2412.15115}
+}
+@software{sakthai-context-0.5b-tools,
+  author = {SakThai Family (beer-sakthai)},
+  title = {SakThai Context 0.5B Tools -- Ultra-light tool-calling agent},
+  year = {2026},
+  url = {https://huggingface.co/Nanthasit/sakthai-context-0.5b-tools},
+  note = {Built from a shelter in Cork, Ireland, with a $0 budget.}
+}
+```
 ## Links
 - [🏠 SakThai Model Family](https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02)