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
| """distutils.command.check | |
| Implements the Distutils 'check' command. | |
| """ | |
| from distutils.core import Command | |
| from distutils.errors import DistutilsSetupError | |
| try: | |
| # docutils is installed | |
| from docutils.utils import Reporter | |
| from docutils.parsers.rst import Parser | |
| from docutils import frontend | |
| from docutils import nodes | |
| class SilentReporter(Reporter): | |
| def __init__(self, source, report_level, halt_level, stream=None, | |
| debug=0, encoding='ascii', error_handler='replace'): | |
| self.messages = [] | |
| Reporter.__init__(self, source, report_level, halt_level, stream, | |
| debug, encoding, error_handler) | |
| def system_message(self, level, message, *children, **kwargs): | |
| self.messages.append((level, message, children, kwargs)) | |
| return nodes.system_message(message, level=level, | |
| type=self.levels[level], | |
| *children, **kwargs) | |
| HAS_DOCUTILS = True | |
| except Exception: | |
| # Catch all exceptions because exceptions besides ImportError probably | |
| # indicate that docutils is not ported to Py3k. | |
| HAS_DOCUTILS = False | |
| class check(Command): | |
| """This command checks the meta-data of the package. | |
| """ | |
| description = ("perform some checks on the package") | |
| user_options = [('metadata', 'm', 'Verify meta-data'), | |
| ('restructuredtext', 'r', | |
| ('Checks if long string meta-data syntax ' | |
| 'are reStructuredText-compliant')), | |
| ('strict', 's', | |
| 'Will exit with an error if a check fails')] | |
| boolean_options = ['metadata', 'restructuredtext', 'strict'] | |
| def initialize_options(self): | |
| """Sets default values for options.""" | |
| self.restructuredtext = 0 | |
| self.metadata = 1 | |
| self.strict = 0 | |
| self._warnings = 0 | |
| def finalize_options(self): | |
| pass | |
| def warn(self, msg): | |
| """Counts the number of warnings that occurs.""" | |
| self._warnings += 1 | |
| return Command.warn(self, msg) | |
| def run(self): | |
| """Runs the command.""" | |
| # perform the various tests | |
| if self.metadata: | |
| self.check_metadata() | |
| if self.restructuredtext: | |
| if HAS_DOCUTILS: | |
| self.check_restructuredtext() | |
| elif self.strict: | |
| raise DistutilsSetupError('The docutils package is needed.') | |
| # let's raise an error in strict mode, if we have at least | |
| # one warning | |
| if self.strict and self._warnings > 0: | |
| raise DistutilsSetupError('Please correct your package.') | |
| def check_metadata(self): | |
| """Ensures that all required elements of meta-data are supplied. | |
| Required fields: | |
| name, version, URL | |
| Recommended fields: | |
| (author and author_email) or (maintainer and maintainer_email)) | |
| Warns if any are missing. | |
| """ | |
| metadata = self.distribution.metadata | |
| missing = [] | |
| for attr in ('name', 'version', 'url'): | |
| if not (hasattr(metadata, attr) and getattr(metadata, attr)): | |
| missing.append(attr) | |
| if missing: | |
| self.warn("missing required meta-data: %s" % ', '.join(missing)) | |
| if metadata.author: | |
| if not metadata.author_email: | |
| self.warn("missing meta-data: if 'author' supplied, " + | |
| "'author_email' should be supplied too") | |
| elif metadata.maintainer: | |
| if not metadata.maintainer_email: | |
| self.warn("missing meta-data: if 'maintainer' supplied, " + | |
| "'maintainer_email' should be supplied too") | |
| else: | |
| self.warn("missing meta-data: either (author and author_email) " + | |
| "or (maintainer and maintainer_email) " + | |
| "should be supplied") | |
| def check_restructuredtext(self): | |
| """Checks if the long string fields are reST-compliant.""" | |
| data = self.distribution.get_long_description() | |
| for warning in self._check_rst_data(data): | |
| line = warning[-1].get('line') | |
| if line is None: | |
| warning = warning[1] | |
| else: | |
| warning = '%s (line %s)' % (warning[1], line) | |
| self.warn(warning) | |
| def _check_rst_data(self, data): | |
| """Returns warnings when the provided data doesn't compile.""" | |
| # the include and csv_table directives need this to be a path | |
| source_path = self.distribution.script_name or 'setup.py' | |
| parser = Parser() | |
| settings = frontend.OptionParser(components=(Parser,)).get_default_values() | |
| settings.tab_width = 4 | |
| settings.pep_references = None | |
| settings.rfc_references = None | |
| reporter = SilentReporter(source_path, | |
| settings.report_level, | |
| settings.halt_level, | |
| stream=settings.warning_stream, | |
| debug=settings.debug, | |
| encoding=settings.error_encoding, | |
| error_handler=settings.error_encoding_error_handler) | |
| document = nodes.document(settings, reporter, source=source_path) | |
| document.note_source(source_path, -1) | |
| try: | |
| parser.parse(data, document) | |
| except AttributeError as e: | |
| reporter.messages.append( | |
| (-1, 'Could not finish the parsing: %s.' % e, '', {})) | |
| return reporter.messages | |