Instructions to use Floobin/TSwifty-SN6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Floobin/TSwifty-SN6 with Transformers:

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

pipe = pipeline("text-generation", model="Floobin/TSwifty-SN6")
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("Floobin/TSwifty-SN6")
model = AutoModelForCausalLM.from_pretrained("Floobin/TSwifty-SN6", 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 Floobin/TSwifty-SN6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Floobin/TSwifty-SN6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Floobin/TSwifty-SN6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Floobin/TSwifty-SN6

SGLang

How to use Floobin/TSwifty-SN6 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 "Floobin/TSwifty-SN6" \
    --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": "Floobin/TSwifty-SN6",
		"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 "Floobin/TSwifty-SN6" \
        --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": "Floobin/TSwifty-SN6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Floobin/TSwifty-SN6 with Docker Model Runner:
```
docker model run hf.co/Floobin/TSwifty-SN6
```

TSwifty-SN6

File size: 962 Bytes

0a594a8

@echo off

rem This file is UTF-8 encoded, so we need to update the current code page while executing it
for /f "tokens=2 delims=:." %%a in ('"%SystemRoot%\System32\chcp.com"') do (
    set _OLD_CODEPAGE=%%a
)
if defined _OLD_CODEPAGE (
    "%SystemRoot%\System32\chcp.com" 65001 > nul
)

set VIRTUAL_ENV=C:\Users\brend\OneDrive\desktop\Udemy\SN6\TSwifty-SN6\venv

if not defined PROMPT set PROMPT=$P$G

if defined _OLD_VIRTUAL_PROMPT set PROMPT=%_OLD_VIRTUAL_PROMPT%
if defined _OLD_VIRTUAL_PYTHONHOME set PYTHONHOME=%_OLD_VIRTUAL_PYTHONHOME%

set _OLD_VIRTUAL_PROMPT=%PROMPT%
set PROMPT=(venv) %PROMPT%

if defined PYTHONHOME set _OLD_VIRTUAL_PYTHONHOME=%PYTHONHOME%
set PYTHONHOME=

if defined _OLD_VIRTUAL_PATH set PATH=%_OLD_VIRTUAL_PATH%
if not defined _OLD_VIRTUAL_PATH set _OLD_VIRTUAL_PATH=%PATH%

set PATH=%VIRTUAL_ENV%\Scripts;%PATH%

:END
if defined _OLD_CODEPAGE (
    "%SystemRoot%\System32\chcp.com" %_OLD_CODEPAGE% > nul
    set _OLD_CODEPAGE=
)