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: 3,223 Bytes
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from distutils import log
import itertools
from setuptools.extern.six.moves import map
flatten = itertools.chain.from_iterable
class Installer:
nspkg_ext = '-nspkg.pth'
def install_namespaces(self):
nsp = self._get_all_ns_packages()
if not nsp:
return
filename, ext = os.path.splitext(self._get_target())
filename += self.nspkg_ext
self.outputs.append(filename)
log.info("Installing %s", filename)
lines = map(self._gen_nspkg_line, nsp)
if self.dry_run:
# always generate the lines, even in dry run
list(lines)
return
with open(filename, 'wt') as f:
f.writelines(lines)
def uninstall_namespaces(self):
filename, ext = os.path.splitext(self._get_target())
filename += self.nspkg_ext
if not os.path.exists(filename):
return
log.info("Removing %s", filename)
os.remove(filename)
def _get_target(self):
return self.target
_nspkg_tmpl = (
"import sys, types, os",
"has_mfs = sys.version_info > (3, 5)",
"p = os.path.join(%(root)s, *%(pth)r)",
"importlib = has_mfs and __import__('importlib.util')",
"has_mfs and __import__('importlib.machinery')",
(
"m = has_mfs and "
"sys.modules.setdefault(%(pkg)r, "
"importlib.util.module_from_spec("
"importlib.machinery.PathFinder.find_spec(%(pkg)r, "
"[os.path.dirname(p)])))"
),
(
"m = m or "
"sys.modules.setdefault(%(pkg)r, types.ModuleType(%(pkg)r))"
),
"mp = (m or []) and m.__dict__.setdefault('__path__',[])",
"(p not in mp) and mp.append(p)",
)
"lines for the namespace installer"
_nspkg_tmpl_multi = (
'm and setattr(sys.modules[%(parent)r], %(child)r, m)',
)
"additional line(s) when a parent package is indicated"
def _get_root(self):
return "sys._getframe(1).f_locals['sitedir']"
def _gen_nspkg_line(self, pkg):
# ensure pkg is not a unicode string under Python 2.7
pkg = str(pkg)
pth = tuple(pkg.split('.'))
root = self._get_root()
tmpl_lines = self._nspkg_tmpl
parent, sep, child = pkg.rpartition('.')
if parent:
tmpl_lines += self._nspkg_tmpl_multi
return ';'.join(tmpl_lines) % locals() + '\n'
def _get_all_ns_packages(self):
"""Return sorted list of all package namespaces"""
pkgs = self.distribution.namespace_packages or []
return sorted(flatten(map(self._pkg_names, pkgs)))
@staticmethod
def _pkg_names(pkg):
"""
Given a namespace package, yield the components of that
package.
>>> names = Installer._pkg_names('a.b.c')
>>> set(names) == set(['a', 'a.b', 'a.b.c'])
True
"""
parts = pkg.split('.')
while parts:
yield '.'.join(parts)
parts.pop()
class DevelopInstaller(Installer):
def _get_root(self):
return repr(str(self.egg_path))
def _get_target(self):
return self.egg_link
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