Instructions to use Nanthasit/sft-out with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nanthasit/sft-out with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Nanthasit/sft-out", device_map="auto")

PEFT
How to use Nanthasit/sft-out with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nanthasit/sft-out with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nanthasit/sft-out"
# 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/sft-out",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nanthasit/sft-out

SGLang

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

Docker Model Runner
How to use Nanthasit/sft-out with Docker Model Runner:
```
docker model run hf.co/Nanthasit/sft-out
```

sft-out / README.md

Commit History

fix: remove phantom combined-v10 references

39cdacd

SakThai Agent commited on about 5 hours ago

chore(card): rewrite sft-out README with full front-matter, sections, and examples

d7b4d11
verified

Nanthasit commited on about 12 hours ago

docs: improve README with structured sections, model-index, limitations, citation, honest bench/verification notes

31b2e65
verified

Nanthasit commited on about 13 hours ago

docs: add explicit Verification section and status note to adapter README

2380905
verified

Nanthasit commited on about 14 hours ago

Enrich sft-out card (1,570 -> 10,941 B): richer YAML tags, badges, adapter/base tables, merge/usage examples, 25-model family table

d1c1380

SakThai Agent commited on about 15 hours ago

SFT bootstrap (r=8, 2 epochs, x4) via ZeroGPU Space

a9015b8
verified

Nanthasit commited on about 19 hours ago

Commit History

fix: remove phantom combined-v10 references 39cdacd

chore(card): rewrite sft-out README with full front-matter, sections, and examples d7b4d11 verified

docs: improve README with structured sections, model-index, limitations, citation, honest bench/verification notes 31b2e65 verified

docs: add explicit Verification section and status note to adapter README 2380905 verified

Enrich sft-out card (1,570 -> 10,941 B): richer YAML tags, badges, adapter/base tables, merge/usage examples, 25-model family table d1c1380