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 os | |
| from distutils import log | |
| import itertools | |
| from setuptools.extern.six.moves import map | |
| flatten = itertools.chain.from_iterable | |
| class Installer: | |
| nspkg_ext = '-nspkg.pth' | |
| def install_namespaces(self): | |
| nsp = self._get_all_ns_packages() | |
| if not nsp: | |
| return | |
| filename, ext = os.path.splitext(self._get_target()) | |
| filename += self.nspkg_ext | |
| self.outputs.append(filename) | |
| log.info("Installing %s", filename) | |
| lines = map(self._gen_nspkg_line, nsp) | |
| if self.dry_run: | |
| # always generate the lines, even in dry run | |
| list(lines) | |
| return | |
| with open(filename, 'wt') as f: | |
| f.writelines(lines) | |
| def uninstall_namespaces(self): | |
| filename, ext = os.path.splitext(self._get_target()) | |
| filename += self.nspkg_ext | |
| if not os.path.exists(filename): | |
| return | |
| log.info("Removing %s", filename) | |
| os.remove(filename) | |
| def _get_target(self): | |
| return self.target | |
| _nspkg_tmpl = ( | |
| "import sys, types, os", | |
| "has_mfs = sys.version_info > (3, 5)", | |
| "p = os.path.join(%(root)s, *%(pth)r)", | |
| "importlib = has_mfs and __import__('importlib.util')", | |
| "has_mfs and __import__('importlib.machinery')", | |
| ( | |
| "m = has_mfs and " | |
| "sys.modules.setdefault(%(pkg)r, " | |
| "importlib.util.module_from_spec(" | |
| "importlib.machinery.PathFinder.find_spec(%(pkg)r, " | |
| "[os.path.dirname(p)])))" | |
| ), | |
| ( | |
| "m = m or " | |
| "sys.modules.setdefault(%(pkg)r, types.ModuleType(%(pkg)r))" | |
| ), | |
| "mp = (m or []) and m.__dict__.setdefault('__path__',[])", | |
| "(p not in mp) and mp.append(p)", | |
| ) | |
| "lines for the namespace installer" | |
| _nspkg_tmpl_multi = ( | |
| 'm and setattr(sys.modules[%(parent)r], %(child)r, m)', | |
| ) | |
| "additional line(s) when a parent package is indicated" | |
| def _get_root(self): | |
| return "sys._getframe(1).f_locals['sitedir']" | |
| def _gen_nspkg_line(self, pkg): | |
| # ensure pkg is not a unicode string under Python 2.7 | |
| pkg = str(pkg) | |
| pth = tuple(pkg.split('.')) | |
| root = self._get_root() | |
| tmpl_lines = self._nspkg_tmpl | |
| parent, sep, child = pkg.rpartition('.') | |
| if parent: | |
| tmpl_lines += self._nspkg_tmpl_multi | |
| return ';'.join(tmpl_lines) % locals() + '\n' | |
| def _get_all_ns_packages(self): | |
| """Return sorted list of all package namespaces""" | |
| pkgs = self.distribution.namespace_packages or [] | |
| return sorted(flatten(map(self._pkg_names, pkgs))) | |
| def _pkg_names(pkg): | |
| """ | |
| Given a namespace package, yield the components of that | |
| package. | |
| >>> names = Installer._pkg_names('a.b.c') | |
| >>> set(names) == set(['a', 'a.b', 'a.b.c']) | |
| True | |
| """ | |
| parts = pkg.split('.') | |
| while parts: | |
| yield '.'.join(parts) | |
| parts.pop() | |
| class DevelopInstaller(Installer): | |
| def _get_root(self): | |
| return repr(str(self.egg_path)) | |
| def _get_target(self): | |
| return self.egg_link | |