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

Libraries

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

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

pipe = pipeline("text-generation", model="Nanthasit/sakthai-context-0.5b-merged")
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-merged")
model = AutoModelForCausalLM.from_pretrained("Nanthasit/sakthai-context-0.5b-merged")
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-merged 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-merged"
# 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-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use Nanthasit/sakthai-context-0.5b-merged 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-merged" \
    --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-merged",
		"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-merged" \
        --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-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

SakThai Context — Merged Model

Model: Nanthasit/sakthai-context-0.5b-merged
Base: Nanthasit/sakthai-context-0.5b (Qwen2.5-0.5B-Instruct)
Adapter: Nanthasit/sakthai-context-0.5b-tools (LoRA r=8, target=q_proj+k_proj+v_proj+o_proj)
Dataset: Nanthasit/sakthai-combined-v3 (~2153 examples)
Training: HF Jobs (T4-small, 3 epochs, LR=2e-4, effective batch size=16)

Evaluation Results

Task	Accuracy	Acc Norm
PIQA	68.0%	66.0%
ARC-Easy	56.0%	60.0%
HellaSwag	40.0%	46.0%
Winogrande	52.0%	-

Using lm-eval v0.4.12 (50 samples per task, 0-shot, CPU). Full report: eval/BENCHMARK.md

Custom Evaluation (SakThai tasks)

Custom SakThai eval: 15/15 tests passed (100%) — multi-turn recall, instruction following, JSON output, tool-calling awareness. Full report: eval/EVAL.md

Downloads last month: 540

Safetensors

Model size

0.5B params

Tensor type

BF16

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

Base model

Nanthasit/sakthai-context-0.5b

Adapter

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Dataset used to train Nanthasit/sakthai-context-0.5b-merged

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Collection including Nanthasit/sakthai-context-0.5b-merged

SakThai Context

Collection

Fine-tuned tool-calling models from the SakThai family — Qwen2.5-based LoRA adapters and merged checkpoints trained on curated SFT datasets. • 7 items • Updated 1 day ago