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 distutils.util import convert_path | |
| from distutils import log | |
| from distutils.errors import DistutilsOptionError | |
| import os | |
| import shutil | |
| from setuptools.extern import six | |
| from setuptools import Command | |
| class rotate(Command): | |
| """Delete older distributions""" | |
| description = "delete older distributions, keeping N newest files" | |
| user_options = [ | |
| ('match=', 'm', "patterns to match (required)"), | |
| ('dist-dir=', 'd', "directory where the distributions are"), | |
| ('keep=', 'k', "number of matching distributions to keep"), | |
| ] | |
| boolean_options = [] | |
| def initialize_options(self): | |
| self.match = None | |
| self.dist_dir = None | |
| self.keep = None | |
| def finalize_options(self): | |
| if self.match is None: | |
| raise DistutilsOptionError( | |
| "Must specify one or more (comma-separated) match patterns " | |
| "(e.g. '.zip' or '.egg')" | |
| ) | |
| if self.keep is None: | |
| raise DistutilsOptionError("Must specify number of files to keep") | |
| try: | |
| self.keep = int(self.keep) | |
| except ValueError as e: | |
| raise DistutilsOptionError("--keep must be an integer") from e | |
| if isinstance(self.match, six.string_types): | |
| self.match = [ | |
| convert_path(p.strip()) for p in self.match.split(',') | |
| ] | |
| self.set_undefined_options('bdist', ('dist_dir', 'dist_dir')) | |
| def run(self): | |
| self.run_command("egg_info") | |
| from glob import glob | |
| for pattern in self.match: | |
| pattern = self.distribution.get_name() + '*' + pattern | |
| files = glob(os.path.join(self.dist_dir, pattern)) | |
| files = [(os.path.getmtime(f), f) for f in files] | |
| files.sort() | |
| files.reverse() | |
| log.info("%d file(s) matching %s", len(files), pattern) | |
| files = files[self.keep:] | |
| for (t, f) in files: | |
| log.info("Deleting %s", f) | |
| if not self.dry_run: | |
| if os.path.isdir(f): | |
| shutil.rmtree(f) | |
| else: | |
| os.unlink(f) | |