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)

# 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=40)
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 / venv /Lib /site-packages /setuptools /_distutils /command /install_data.py

Floobin

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	"""distutils.command.install_data

	Implements the Distutils 'install_data' command, for installing
	platform-independent data files."""

	# contributed by Bastian Kleineidam

	import os
	from distutils.core import Command
	from distutils.util import change_root, convert_path

	class install_data(Command):

	description = "install data files"

	user_options = [
	('install-dir=', 'd',
	"base directory for installing data files "
	"(default: installation base dir)"),
	('root=', None,
	"install everything relative to this alternate root directory"),
	('force', 'f', "force installation (overwrite existing files)"),
	]

	boolean_options = ['force']

	def initialize_options(self):
	self.install_dir = None
	self.outfiles = []
	self.root = None
	self.force = 0
	self.data_files = self.distribution.data_files
	self.warn_dir = 1

	def finalize_options(self):
	self.set_undefined_options('install',
	('install_data', 'install_dir'),
	('root', 'root'),
	('force', 'force'),
	)

	def run(self):
	self.mkpath(self.install_dir)
	for f in self.data_files:
	if isinstance(f, str):
	# it's a simple file, so copy it
	f = convert_path(f)
	if self.warn_dir:
	self.warn("setup script did not provide a directory for "
	"'%s' -- installing right in '%s'" %
	(f, self.install_dir))
	(out, _) = self.copy_file(f, self.install_dir)
	self.outfiles.append(out)
	else:
	# it's a tuple with path to install to and a list of files
	dir = convert_path(f[0])
	if not os.path.isabs(dir):
	dir = os.path.join(self.install_dir, dir)
	elif self.root:
	dir = change_root(self.root, dir)
	self.mkpath(dir)

	if f[1] == []:
	# If there are no files listed, the user must be
	# trying to create an empty directory, so add the
	# directory to the list of output files.
	self.outfiles.append(dir)
	else:
	# Copy files, adding them to the list of output files.
	for data in f[1]:
	data = convert_path(data)
	(out, _) = self.copy_file(data, dir)
	self.outfiles.append(out)

	def get_inputs(self):
	return self.data_files or []

	def get_outputs(self):
	return self.outfiles