Instructions to use Floobin/TSwifty-SN6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 2,822 Bytes
0a594a8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | """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
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