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
```

SakThai Context 0.5B Tools

Ultra-light tool-calling agent · Qwen2.5-0.5B fine-tune · runs in ~1 GB RAM

SakThai Context 0.5B Tools is a prompt-masked SFT of Qwen2.5-0.5B-Instruct optimized for browser/tool calling. It achieves 91.2% selection accuracy on SakThai Bench v2, with 0% degenerate outputs in multi-trial evaluation.

Model Description

SakThai Context 0.5B Tools is a prompt-masked supervised fine-tune of Qwen/Qwen2.5-0.5B-Instruct focused on reliable tool/function calling in conversational agents. The model is trained to select the correct tool, generate valid JSON-style arguments, and avoid degenerate outputs. It is optimized for edge deployment and can run on consumer hardware with ~1 GB RAM.

Key points:

Base: Qwen/Qwen2.5-0.5B-Instruct
Training: prompt-masked SFT on tool-calling traces from Nanthasit/sakthai-combined-v7
Primary use: lightweight agents, on-device assistants, Raspberry Pi / edge deployments
License: Apache-2.0

Quick Start — Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Nanthasit/sakthai-context-0.5b-tools"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather",
            "parameters": {
                "type": "object",
                "properties": {"location": {"type": "string"}},
                "required": ["location"],
            },
        }
    }
]

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What's the weather in Tokyo?"},
]

text = tokenizer.apply_chat_template(messages, tools=tools, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.01, top_p=0.9)
response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)

Quick Start — llama.cpp / Ollama

# Convert with llama.cpp and run locally
llama-quantize ./sakthai-context-0.5b-tools-f16.gguf ./model-q4_k_m.gguf Q4_K_M
ollama create sakthai-context-0.5b-tools -f Modelfile
ollama run sakthai-context-0.5b-tools

Usage notes

For tool calling, always use apply_chat_template(..., tools=tools, tokenize=False, add_generation_prompt=True) so the model receives the proper <tools> block.
If you want stricter outputs, reduce temperature further, e.g. 0.0.
For CPU-only inference, set device_map="cpu"; GPU/MPS/CPU auto-detection works with device_map="auto".

Architecture & Config

Field	Value
Architecture	`Qwen2ForCausalLM`
Model type	`qwen2`
Vocab size	`151936`
Hidden size	`896`
Layers	`24`
Attention heads	`14`
KV heads	`2`
Intermediate size	`4864`
Activation	`silu`
Max position	`32768`
Transformers	`5.14.1`

Benchmarks

Metric	Value	Verified
Selection Accuracy	91.2%	true
Arguments Accuracy	45.7%	true
Strict Accuracy	45.7%	true
Held-Out Tool Accuracy	87.8%	true
Degenerate Outputs	0%	true

Evidence: .eval_results/sakthai-bench-v2.yaml in repo.

Limitations

0.5B parameter scale limits reasoning depth; arguments accuracy is lower than selection accuracy.
Tool schema adherence degrades on nested arguments and long context traces.
Current weights are merged; if you need the unmerged adapter, use Nanthasit/sakthai-context-0.5b-tools-sft or Nanthasit/sakthai-context-0.5b-tools-sft-v2.

Citation

If you use this model, please cite the SakThai model family and benchmark:

@misc{sakthai2025context05btools,
  title  = {SakThai Context 0.5B Tools},
  author = {Nanthasit},
  year   = {2026},
  url    = {https://huggingface.co/Nanthasit/sakthai-context-0.5b-tools}
}

SakThai Family

Repo	Downloads	Size	Pipeline
Nanthasit/sakthai-context-1.5b-merged	1855	~4.07 GB	text-generation
Nanthasit/sakthai-context-0.5b-merged	1692	~1.39 GB	text-generation
Nanthasit/sakthai-context-7b-merged	1024	~15.23 GB	text-generation
Nanthasit/sakthai-embedding-multilingual	627	~471 MB	sentence-similarity
Nanthasit/sakthai-context-7b-128k	610	—	text-generation
Nanthasit/sakthai-context-7b-tools	489	~20 MB	text-generation
Nanthasit/sakthai-context-1.5b-tools	477	~8.7 MB	text-generation
Nanthasit/sakthai-context-1.5b-merged-v2	337	~3.09 GB	text-generation
Nanthasit/sakthai-vision-7b	315	~4.71 GB	image-text-to-text
Nanthasit/sakthai-plus-1.5b-lora	306	~74 MB	text-generation
Nanthasit/sakthai-context-0.5b-tools	251	~1.0 GB	text-generation
Nanthasit/sakthai-tts-model	248	~143 MB	text-to-speech
Nanthasit/sakthai-plus-1.5b	244	~3.09 GB	text-generation
Nanthasit/sakthai-context-1.5b-tools-v2	173	~74 MB	text-generation
Nanthasit/sakthai-coder-1.5b	151	~1.12 GB	text-generation
Nanthasit/sakthai-coder-browser	54	~3.09 GB	text-generation
Nanthasit/sakthai-coder-browser-gguf	35	~7.11 GB	text-generation
Nanthasit/sakthai-embedding	23	~110 MB	sentence-similarity
Nanthasit/sakthai-coder-browser-lora	21	~74 MB	text-generation

Download counts and sizes were verified from the Hub API at upload time.

Downloads last month: 725

Safetensors

Model size

0.5B params

Tensor type

BF16

Model tree for Nanthasit/sakthai-context-0.5b-tools

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Finetuned

(925)

this model

Datasets used to train Nanthasit/sakthai-context-0.5b-tools

Spaces using Nanthasit/sakthai-context-0.5b-tools 2

Collections including Nanthasit/sakthai-context-0.5b-tools

SakThai Context Models

Collection

All SakThai context fine-tunes: 0.5B to 7B, merged + adapters, 128K context, code variant. Built on Qwen2.5. • 8 items • Updated 7 days ago

SakThai Model Family

Collection

SakThai family: 47 curated items (26 models, 15 datasets, 6 spaces). • 38 items • Updated about 10 hours ago

Evaluation results

Selection Accuracy on SakThai Bench v2 (500 rows, scorer multiset-selection-v2)
self-reported

91.200
Arguments Accuracy on SakThai Bench v2 (500 rows, scorer multiset-selection-v2)
self-reported

45.700
Strict Accuracy on SakThai Bench v2 (500 rows, scorer multiset-selection-v2)
self-reported

45.700
Held-Out Tool Accuracy on SakThai Bench v2 (500 rows, scorer multiset-selection-v2)
self-reported

87.800
Degenerate Outputs on SakThai Bench v2 (500 rows, scorer multiset-selection-v2)
self-reported

0.000