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 Jul 31

Commit

9719da5

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1 Parent(s): ce53eb0

Upload .eval_results/benchmark-20260731_043634.yaml with huggingface_hub

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.eval_results/benchmark-20260731_043634.yaml +87 -0

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	@@ -0,0 +1,87 @@

+model: Nanthasit/sakthai-plus-1.5b
+benchmark_ts: '2026-07-31T04:36:34Z'
+backend: llama.cpp-gguf-q4_k_m
+quantization: q4_k_m
+prompt_type: tool_calling_send_email
+prompt_length_chars: 1290
+prompt: '<|im_start|>system
+  You are Qwen, created by Alibaba Cloud. You are a helpful assistant.
+  # Tools
+  Yo...'
+trials: 3
+total_time_s: 47.56
+input_tokens: 308
+avg_generation_tps: 20.7
+has_tool_call_any: true
+has_tool_call_all: true
+has_valid_json_any: true
+has_valid_json_all: true
+has_correct_answer_any: true
+has_correct_answer_all: true
+trials_detail:
+- seed: 7
+  output_tokens: 63
+  output_length: 231
+  generation_tps: 20.9
+  prompt_tps: 67.0
+  has_tool_call: true
+  has_valid_json: true
+  has_correct_answer: true
+  tool_name: send_email
+  tool_args:
+    to: Beer@Nanthasit.com
+    subject: Plus 1.5B status
+    body: Hi Beer, I wanted to check the benchmark results for the SakThai Plus model.
+      Thanks!
+  response_preview: '<tool_call>
+    {"name": "send_email", "arguments": "{\"to\": \"Beer@Nanthasit.com\", \"subject\":
+    \"Plus 1.5B status\", \"body\": \"Hi Beer, I wanted to check the benchmark results
+    for the SakThai Plus m'
+- seed: 42
+  output_tokens: 65
+  output_length: 243
+  generation_tps: 20.8
+  prompt_tps: 71.0
+  has_tool_call: true
+  has_valid_json: true
+  has_correct_answer: true
+  tool_name: send_email
+  tool_args:
+    to: Beer@Nanthasit.com
+    subject: Plus 1.5B status
+    body: Hi Beer, I wanted to check the results for the SakThai Plus model. Everything
+      looks good so far.
+  response_preview: '<tool_call>
+    {"name": "send_email", "arguments": "{\"to\": \"Beer@Nanthasit.com\", \"subject\":
+    \"Plus 1.5B status\", \"body\": \"Hi Beer, I wanted to check the results for the
+    SakThai Plus model. Ever'
+- seed: 1337
+  output_tokens: 66
+  output_length: 253
+  generation_tps: 20.4
+  prompt_tps: 70.5
+  has_tool_call: true
+  has_valid_json: true
+  has_correct_answer: true
+  tool_name: send_email
+  tool_args:
+    to: Beer@Nanthasit.com
+    subject: Plus 1.5B status
+    body: Hi Beer, I wanted to check the benchmark results for the SakThai Plus model.
+      Everything looks good so far.
+  response_preview: '<tool_call>
+    {"name": "send_email", "arguments": "{\"to\": \"Beer@Nanthasit.com\", \"subject\":
+    \"Plus 1.5B status\", \"body\": \"Hi Beer, I wanted to check the benchmark results
+    for the SakThai Plus m'
+device: cpu
+threads: 2
+router_probe_status: 400
+router_probe_error: Model not supported by provider hf-inference