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 30

Commit

4e00d14

verified ·

1 Parent(s): 740ccae

Delete .eval_results/health-check.yaml with huggingface_hub

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.eval_results/health-check.yaml +0 -143

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@@ -1,143 +0,0 @@
-# Model Health Check - Nanthasit/sakthai-plus-1.5b
-# Generated: 2026-07-30 22:24:54 UTC (cron)
-# Tool: sakthai-agent-health-diagnostics
-model:
-  id: Nanthasit/sakthai-plus-1.5b
-  pipeline_tag: text-generation
-  library_name: transformers
-  private: false
-  gated: false
-  disabled: false
-  author: Nanthasit
-  sha: 1609df911f740e613733911cde720ec3680cb6ed
-  created_at: "2026-07-30T13:02:40.000Z"
-  last_modified: "2026-07-30T22:19:36.000Z"
-  days_since_update: 0.0
-  days_since_creation: 0.39
-metrics:
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-  likes: 0
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-architecture:
-  model_type: qwen2
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-  num_attention_heads: 12
-  num_kv_heads: 2
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-  torch_dtype: N/A
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-  bos_token_id: 151643
-  top_k: 20
-  top_p: 0.8
-  temperature: 0.7
-  repetition_penalty: 1.1
-benchmarks:
-  - benchmark: "SakThai Bench v2 (500 rows, scorer multiset-selection-v2)"
-    dataset: Nanthasit/sakthai-bench-v2
-    metrics:
-      Selection Accuracy: pending
-      Degenerate Rate: 0
-    verified: false
-tags:
-  - transformers
-  - safetensors
-  - qwen2
-  - text-generation
-  - qwen2.5
-  - sakthai
-  - plus
-  - tool-calling
-  - conversational
-  - function-calling
-  - merged
-  - rslor
-  - house-of-sak
-  - family
-  - en
-  - dataset:Nanthasit/sakthai-combined-v7
-  - dataset:Nanthasit/sakthai-combined-v8
-  - base_model:Qwen/Qwen2.5-1.5B-Instruct
-  - base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct
-  - license:apache-2.0
-  - model-index
-  - eval-results
-  - text-generation-inference
-  - endpoints_compatible
-  - region:us
-card_data:
-  license: apache-2.0
-  base_model: Qwen/Qwen2.5-1.5B-Instruct
-  tags_count: 10
-health_flags:
-  - "Zero downloads - model is brand new (created today)"
-  - "Zero likes"
-  - "Benchmark 'Selection Accuracy' is 'pending' - needs verification"
-  - "Fine-tuned from Qwen/Qwen2.5-1.5B-Instruct"
-health_score:
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-  raw_score: 65
-assessment:
-  health: GOOD
-  summary: "downloads=0, likes=0, benchmarks=pending, age=0.39d, health=GOOD(65/100)"
-  recommendations:
-    - "Share model link in SakThai channels to drive adoption"
-    - "Run LightEval or HF Community Evals to fill benchmark values"