Text Generation
Transformers
Safetensors
PEFT
English
Generated from Trainer
trl
sft
lora
adapter
finetuned
conversational
assistant
tool-calling
function-calling
qwen
qwen2.5
slm
small-language-model
sakthai
house-of-sak
Eval Results
Eval Results (legacy)
Instructions to use Nanthasit/sft-out with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Commit History
Add eval result: .eval_results/cron-eval-sft-out-20260801T060225Z.yaml 20ffd05 verified
fix: remove phantom combined-v10 references 39cdacd
SakThai Agent commited on
Add eval snapshot 20260801T002110Z 760dd7f verified
cron: add metadata eval result for sft-out e4146c4 verified
chore(card): rewrite sft-out README with full front-matter, sections, and examples d7b4d11 verified
Add eval result: sft-out metadata snapshot (hf-eval-updater cron) de90214 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
SakThai Agent commited on