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
| import contextlib | |
| import re | |
| from dataclasses import dataclass | |
| from typing import Dict, Iterator, NoReturn, Optional, Tuple, Union | |
| from .specifiers import Specifier | |
| class Token: | |
| name: str | |
| text: str | |
| position: int | |
| class ParserSyntaxError(Exception): | |
| """The provided source text could not be parsed correctly.""" | |
| def __init__( | |
| self, | |
| message: str, | |
| *, | |
| source: str, | |
| span: Tuple[int, int], | |
| ) -> None: | |
| self.span = span | |
| self.message = message | |
| self.source = source | |
| super().__init__() | |
| def __str__(self) -> str: | |
| marker = " " * self.span[0] + "~" * (self.span[1] - self.span[0]) + "^" | |
| return "\n ".join([self.message, self.source, marker]) | |
| DEFAULT_RULES: "Dict[str, Union[str, re.Pattern[str]]]" = { | |
| "LEFT_PARENTHESIS": r"\(", | |
| "RIGHT_PARENTHESIS": r"\)", | |
| "LEFT_BRACKET": r"\[", | |
| "RIGHT_BRACKET": r"\]", | |
| "SEMICOLON": r";", | |
| "COMMA": r",", | |
| "QUOTED_STRING": re.compile( | |
| r""" | |
| ( | |
| ('[^']*') | |
| | | |
| ("[^"]*") | |
| ) | |
| """, | |
| re.VERBOSE, | |
| ), | |
| "OP": r"(===|==|~=|!=|<=|>=|<|>)", | |
| "BOOLOP": r"\b(or|and)\b", | |
| "IN": r"\bin\b", | |
| "NOT": r"\bnot\b", | |
| "VARIABLE": re.compile( | |
| r""" | |
| \b( | |
| python_version | |
| |python_full_version | |
| |os[._]name | |
| |sys[._]platform | |
| |platform_(release|system) | |
| |platform[._](version|machine|python_implementation) | |
| |python_implementation | |
| |implementation_(name|version) | |
| |extra | |
| )\b | |
| """, | |
| re.VERBOSE, | |
| ), | |
| "SPECIFIER": re.compile( | |
| Specifier._operator_regex_str + Specifier._version_regex_str, | |
| re.VERBOSE | re.IGNORECASE, | |
| ), | |
| "AT": r"\@", | |
| "URL": r"[^ \t]+", | |
| "IDENTIFIER": r"\b[a-zA-Z0-9][a-zA-Z0-9._-]*\b", | |
| "VERSION_PREFIX_TRAIL": r"\.\*", | |
| "VERSION_LOCAL_LABEL_TRAIL": r"\+[a-z0-9]+(?:[-_\.][a-z0-9]+)*", | |
| "WS": r"[ \t]+", | |
| "END": r"$", | |
| } | |
| class Tokenizer: | |
| """Context-sensitive token parsing. | |
| Provides methods to examine the input stream to check whether the next token | |
| matches. | |
| """ | |
| def __init__( | |
| self, | |
| source: str, | |
| *, | |
| rules: "Dict[str, Union[str, re.Pattern[str]]]", | |
| ) -> None: | |
| self.source = source | |
| self.rules: Dict[str, re.Pattern[str]] = { | |
| name: re.compile(pattern) for name, pattern in rules.items() | |
| } | |
| self.next_token: Optional[Token] = None | |
| self.position = 0 | |
| def consume(self, name: str) -> None: | |
| """Move beyond provided token name, if at current position.""" | |
| if self.check(name): | |
| self.read() | |
| def check(self, name: str, *, peek: bool = False) -> bool: | |
| """Check whether the next token has the provided name. | |
| By default, if the check succeeds, the token *must* be read before | |
| another check. If `peek` is set to `True`, the token is not loaded and | |
| would need to be checked again. | |
| """ | |
| assert ( | |
| self.next_token is None | |
| ), f"Cannot check for {name!r}, already have {self.next_token!r}" | |
| assert name in self.rules, f"Unknown token name: {name!r}" | |
| expression = self.rules[name] | |
| match = expression.match(self.source, self.position) | |
| if match is None: | |
| return False | |
| if not peek: | |
| self.next_token = Token(name, match[0], self.position) | |
| return True | |
| def expect(self, name: str, *, expected: str) -> Token: | |
| """Expect a certain token name next, failing with a syntax error otherwise. | |
| The token is *not* read. | |
| """ | |
| if not self.check(name): | |
| raise self.raise_syntax_error(f"Expected {expected}") | |
| return self.read() | |
| def read(self) -> Token: | |
| """Consume the next token and return it.""" | |
| token = self.next_token | |
| assert token is not None | |
| self.position += len(token.text) | |
| self.next_token = None | |
| return token | |
| def raise_syntax_error( | |
| self, | |
| message: str, | |
| *, | |
| span_start: Optional[int] = None, | |
| span_end: Optional[int] = None, | |
| ) -> NoReturn: | |
| """Raise ParserSyntaxError at the given position.""" | |
| span = ( | |
| self.position if span_start is None else span_start, | |
| self.position if span_end is None else span_end, | |
| ) | |
| raise ParserSyntaxError( | |
| message, | |
| source=self.source, | |
| span=span, | |
| ) | |
| def enclosing_tokens( | |
| self, open_token: str, close_token: str, *, around: str | |
| ) -> Iterator[None]: | |
| if self.check(open_token): | |
| open_position = self.position | |
| self.read() | |
| else: | |
| open_position = None | |
| yield | |
| if open_position is None: | |
| return | |
| if not self.check(close_token): | |
| self.raise_syntax_error( | |
| f"Expected matching {close_token} for {open_token}, after {around}", | |
| span_start=open_position, | |
| ) | |
| self.read() | |