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: 1,431 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 | # This file is dual licensed under the terms of the Apache License, Version
# 2.0, and the BSD License. See the LICENSE file in the root of this repository
# for complete details.
class InfinityType:
def __repr__(self) -> str:
return "Infinity"
def __hash__(self) -> int:
return hash(repr(self))
def __lt__(self, other: object) -> bool:
return False
def __le__(self, other: object) -> bool:
return False
def __eq__(self, other: object) -> bool:
return isinstance(other, self.__class__)
def __gt__(self, other: object) -> bool:
return True
def __ge__(self, other: object) -> bool:
return True
def __neg__(self: object) -> "NegativeInfinityType":
return NegativeInfinity
Infinity = InfinityType()
class NegativeInfinityType:
def __repr__(self) -> str:
return "-Infinity"
def __hash__(self) -> int:
return hash(repr(self))
def __lt__(self, other: object) -> bool:
return True
def __le__(self, other: object) -> bool:
return True
def __eq__(self, other: object) -> bool:
return isinstance(other, self.__class__)
def __gt__(self, other: object) -> bool:
return False
def __ge__(self, other: object) -> bool:
return False
def __neg__(self: object) -> InfinityType:
return Infinity
NegativeInfinity = NegativeInfinityType()
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