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
| from __future__ import annotations | |
| import abc | |
| import hashlib | |
| from typing import TYPE_CHECKING | |
| from fsspec.implementations.local import make_path_posix | |
| if TYPE_CHECKING: | |
| from typing import Any | |
| class AbstractCacheMapper(abc.ABC): | |
| """Abstract super-class for mappers from remote URLs to local cached | |
| basenames. | |
| """ | |
| def __call__(self, path: str) -> str: | |
| ... | |
| def __eq__(self, other: Any) -> bool: | |
| # Identity only depends on class. When derived classes have attributes | |
| # they will need to be included. | |
| return isinstance(other, type(self)) | |
| def __hash__(self) -> int: | |
| # Identity only depends on class. When derived classes have attributes | |
| # they will need to be included. | |
| return hash(type(self)) | |
| class BasenameCacheMapper(AbstractCacheMapper): | |
| """Cache mapper that uses the basename of the remote URL and a fixed number | |
| of directory levels above this. | |
| The default is zero directory levels, meaning different paths with the same | |
| basename will have the same cached basename. | |
| """ | |
| def __init__(self, directory_levels: int = 0): | |
| if directory_levels < 0: | |
| raise ValueError( | |
| "BasenameCacheMapper requires zero or positive directory_levels" | |
| ) | |
| self.directory_levels = directory_levels | |
| # Separator for directories when encoded as strings. | |
| self._separator = "_@_" | |
| def __call__(self, path: str) -> str: | |
| path = make_path_posix(path) | |
| prefix, *bits = path.rsplit("/", self.directory_levels + 1) | |
| if bits: | |
| return self._separator.join(bits) | |
| else: | |
| return prefix # No separator found, simple filename | |
| def __eq__(self, other: Any) -> bool: | |
| return super().__eq__(other) and self.directory_levels == other.directory_levels | |
| def __hash__(self) -> int: | |
| return super().__hash__() ^ hash(self.directory_levels) | |
| class HashCacheMapper(AbstractCacheMapper): | |
| """Cache mapper that uses a hash of the remote URL.""" | |
| def __call__(self, path: str) -> str: | |
| return hashlib.sha256(path.encode()).hexdigest() | |
| def create_cache_mapper(same_names: bool) -> AbstractCacheMapper: | |
| """Factory method to create cache mapper for backward compatibility with | |
| ``CachingFileSystem`` constructor using ``same_names`` kwarg. | |
| """ | |
| if same_names: | |
| return BasenameCacheMapper() | |
| else: | |
| return HashCacheMapper() | |