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

Libraries

How to use Nanthasit/sakthai-context-1.5b-merged with Transformers:

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

pipe = pipeline("text-generation", model="Nanthasit/sakthai-context-1.5b-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-1.5b-merged")
model = AutoModelForCausalLM.from_pretrained("Nanthasit/sakthai-context-1.5b-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-1.5b-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-1.5b-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-1.5b-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nanthasit/sakthai-context-1.5b-merged

SGLang

How to use Nanthasit/sakthai-context-1.5b-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-1.5b-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-1.5b-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-1.5b-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-1.5b-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

SakThai Context 1.5B

Fine-tuned from Qwen2.5-1.5B-Instruct on the SakThai combined dataset for tool-calling, multi-turn context, and instruction-following capabilities. Designed as the reasoning backbone for the SakThai agent (running on Hermes Agent framework).

Model Details

Property	Value
Base Model	Qwen/Qwen2.5-1.5B-Instruct
Architecture	Qwen2 (decoder-only transformer)
Hidden Size	1536
Layers	28
Attention Heads	12
Intermediate Size	8960
Vocab Size	151936
Fine-tuning Method	LoRA (r=16, α=32, dropout=0.1)
Target Modules	q_proj, k_proj, v_proj, o_proj
Training Steps	220
Training Duration	~39 minutes (4 epochs on 974 examples)
License	Apache 2.0

Training

Base model: Qwen/Qwen2.5-1.5B-Instruct
Dataset: Nanthasit/sakthai-combined-v4 — 974 training + 51 test examples covering 25 canonical tool schemas
Method: LoRA via PEFT (rank=16, alpha=32, dropout=0.1) on q/k/v/o projections
Optimizer: AdamW, linear schedule, 220 steps

The LoRA adapter weights are available at Nanthasit/sakthai-context-1.5b-tools.

Evaluation

45/45 tests passed (100%) across 3 runs (15 tests/run).

Category	Tests	Pass Rate
Basic (greeting, identity)	6	✅ 100%
Multi-turn (name recall, context, preference)	9	✅ 100%
Instruction following	6	✅ 100%
Tool calling	6	✅ 100%
Reasoning (math, coding, explanation)	6	✅ 100%
Format adherence (JSON, markdown, arrays)	12	✅ 100%

Full eval report: eval/EVAL.md

Usage

Via Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Nanthasit/sakthai-context-1.5b-merged")
tokenizer = AutoTokenizer.from_pretrained("Nanthasit/sakthai-context-1.5b-merged")

messages = [{"role": "user", "content": "What's the capital of Japan?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Via Inference Client

from huggingface_hub import InferenceClient

client = InferenceClient("Nanthasit/sakthai-context-1.5b-merged")
response = client.chat_completion(
    messages=[{"role": "user", "content": "Hello!"}],
    max_tokens=256
)
print(response.choices[0].message.content)

Merging the Adapter

from peft import PeftModel
from transformers import AutoModelForCausalLM

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct")
model = PeftModel.from_pretrained(base, "Nanthasit/sakthai-context-1.5b-tools")
merged = model.merge_and_unload()
merged.save_pretrained("./sakthai-context-1.5b-merged")

Limitations

Fine-tuned primarily for tool-calling and structured output; general knowledge remains at Qwen2.5-1.5B-Instruct baseline level.
Tested on CPU — performance on GPU inference may produce slightly different output distributions.
Best suited for agentic workflows with well-defined tool schemas. Complex multi-hop reasoning may require a larger base model.

Bias, Risks & Safety

This model is fine-tuned from Qwen2.5-1.5B-Instruct and inherits its base strengths and limitations. As a small language model (1.5B parameters), it may exhibit:

Factual inaccuracies on niche or recent topics
Biases present in the base model's pre-training data
Limited performance on tasks requiring long context (>2K tokens) or deep multi-step reasoning

Deploy with appropriate guardrails for any user-facing application.

Citation

@misc{sakthai-context-1.5b,
  author = {Nanthasit},
  title = {SakThai Context 1.5B — Tool-Calling Fine-Tune},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/Nanthasit/sakthai-context-1.5b-merged}}
}