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

Libraries

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

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

pipe = pipeline("text-generation", model="Nanthasit/sakthai-plus-1.5b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nanthasit/sakthai-plus-1.5b")
model = AutoModelForCausalLM.from_pretrained("Nanthasit/sakthai-plus-1.5b", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

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

SGLang

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

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

Nanthasit commited on Aug 2

Commit

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@@ -115,25 +115,6 @@ model-index:
 | held_out | - | 16.07 | 16.07 | 16.07 |
-<p align="center">
-  <img src="https://img.shields.io/badge/dynamic/json?url=https%3A//huggingface.co/api/models/Nanthasit/sakthai-plus-1.5b&query=%24.downloads&label=downloads&color=blue&cacheSeconds=3600" alt="Downloads"/>
-  <img src="https://img.shields.io/badge/license-Apache%202.0-green" alt="License"/>
-  <img src="https://img.shields.io/badge/params-1.54B-blueviolet" alt="Params"/>
-  <a href="https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02"><img src="https://img.shields.io/badge/%F0%9F%8F%A0-SakThai%20Family-6644cc" alt="Collection"/></a>
-  <a href="https://huggingface.co/spaces/Nanthasit/sakthai-leaderboard"><img src="https://img.shields.io/badge/%F0%9F%93%8A-Leaderboard-ff6b6b" alt="Leaderboard"/></a>
-</p>
-<h1 align="center">SakThai Plus 1.5B</h1>
-<p align="center"><em>rsLoRA-merged instruct model for tool calling and agentic workflows</em></p>
-**SakThai Plus 1.5B** is a **merged** rsLoRA fine-tune of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct), optimized for structured tool-calling and instruction-following.
-- **Base model:** Qwen/Qwen2.5-1.5B-Instruct
-- **Method:** rsLoRA → merged full weights
-- **Context length:** 32,768 tokens
-- **License:** Apache 2.0
-- **Format:** Qwen2.5 chat template
 ## Training Data
 | Dataset | Rows | Description |

 | held_out | - | 16.07 | 16.07 | 16.07 |
 ## Training Data
 | Dataset | Rows | Description |