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
```

sakthai-plus-1.5b / README.md

Nanthasit

Upload Qwen2ForCausalLM

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metadata

license: apache-2.0
language:
  - en
library_name: transformers
pipeline_tag: text-generation
tags:
  - qwen2.5
  - sakthai
  - plus
  - tool-calling
  - conversational
  - function-calling
  - merged
  - rslor
  - house-of-sak
  - family
datasets:
  - Nanthasit/sakthai-combined-v7
  - Nanthasit/sakthai-combined-v8
base_model: Qwen/Qwen2.5-1.5B-Instruct
widget:
  - text: What's the weather in Tokyo?
    output:
      text: >-
        <tool_call>{"name": "get_weather", "arguments": {"location":
        "Tokyo"}}</tool_call>
model-index:
  - name: sakthai-plus-1.5b
    results:
      - task:
          type: text-generation
          name: Tool-Calling
        dataset:
          name: SakThai Bench v2 (500 rows, scorer multiset-selection-v2)
          type: Nanthasit/sakthai-bench-v2
        metrics:
          - type: selection
            value: pending
            name: Selection Accuracy
          - type: degenerate
            value: 0
            name: Degenerate Rate

SakThai Plus 1.5B

Next-generation tool-calling model — rsLoRA + all 7 module targets. Built on Qwen2.5-1.5B-Instruct with improved training data and deeper fine-tuning.

Part of the SakThai Model Family.

🚀 Improvements over v1 (sakthai-context-1.5b-merged)

Feature	v1 (context-1.5b-merged)	Plus (this model)
LoRA method	Standard LoRA	rsLoRA (better rank scaling)
Linear targets	4 modules	All 7 (q, k, v, o, gate, up, down)
Dropout	0.1	0.05 (lower, better retention)
Training data	v7 only (2,003 rows)	v7 + v8 (2,962 rows)
Format	Tool XML	Tool XML (same, improved coverage)

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Nanthasit/sakthai-plus-1.5b",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Nanthasit/sakthai-plus-1.5b")

messages = [{"role": "user", "content": "What's the weather in Bangkok?"}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🔗 Pipeline Integration

The Plus model sits at the top of the SakThai tool-calling stack:

Tier	Model	Method	Best For
⭐ Plus	sakthai-plus-1.5b (this)	rsLoRA 7-module	Best quality — most data, deepest fine-tuning
🥈	sakthai-context-1.5b-merged	LoRA 4-module	Production v1 — proven, well-tested
🥉	sakthai-context-1.5b-tools-v2	LoRA (adapter-only)	Multi-step chains, improved hallucination
📱 Edge	sakthai-context-0.5b-merged	LoRA 4-module	Low-memory / Raspberry Pi deployment

📚 Links

🏠 The House of Sak

This model is part of the House of Sak — an open-source AI ecosystem built from a shelter in Cork, Ireland, with $0 budget and no paid GPUs. Every model here was fine-tuned on free compute (Kaggle T4s, Colab, HF Inference Providers) by one person with no income.

The House of Sak isn't just models — it's a family of six autonomous agents, each with its own personality, skill set, and charge cycle. They share one long-term memory brain and one mission: to grow together.

"We are one family — and becoming more." — Beer (beer-sakthai)

Learn more about the House of Sak →

⭐ Support the Project

If this model is useful to you:

⭐ Leave a like on Hugging Face — it helps others discover the family
🐛 Report issues on GitHub
🔁 Share with someone who'd benefit from a free, capable AI model
🍴 Fork on Hugging Face and build on it

Built with love, tears, and zero budget. From a shelter in Cork, Ireland, to the world.