Text Generation
Transformers
Safetensors
PyTorch
Kashmiri
ksbyte
kashmiri
byte-level
causal-lm
spacebyte
custom_code
Eval Results (legacy)
Instructions to use Omarrran/ks-byte-lm-spacebyte-transformers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Omarrran/ks-byte-lm-spacebyte-transformers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Omarrran/ks-byte-lm-spacebyte-transformers", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Omarrran/ks-byte-lm-spacebyte-transformers", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Omarrran/ks-byte-lm-spacebyte-transformers with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Omarrran/ks-byte-lm-spacebyte-transformers" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Omarrran/ks-byte-lm-spacebyte-transformers", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Omarrran/ks-byte-lm-spacebyte-transformers
- SGLang
How to use Omarrran/ks-byte-lm-spacebyte-transformers 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 "Omarrran/ks-byte-lm-spacebyte-transformers" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Omarrran/ks-byte-lm-spacebyte-transformers", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "Omarrran/ks-byte-lm-spacebyte-transformers" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Omarrran/ks-byte-lm-spacebyte-transformers", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Omarrran/ks-byte-lm-spacebyte-transformers with Docker Model Runner:
docker model run hf.co/Omarrran/ks-byte-lm-spacebyte-transformers
| import json | |
| from pathlib import Path | |
| from typing import List, Optional | |
| from transformers import PreTrainedTokenizer | |
| class KsByteTokenizer(PreTrainedTokenizer): | |
| """UTF-8 byte tokenizer for ks_byte_lm. | |
| IDs 0..255 are raw UTF-8 byte values; 256/257/258 are BOS/EOS/PAD. | |
| """ | |
| model_input_names = ["input_ids", "attention_mask"] | |
| def __init__( | |
| self, | |
| vocab_file: Optional[str] = None, | |
| zwnj_policy="keep", | |
| digit_policy="keep_native", | |
| remove_diacritics=False, | |
| **kwargs, | |
| ): | |
| self.byte_vocab = 256 | |
| self.vocab = {f"byte_{i}": i for i in range(256)} | |
| self.vocab.update({"<bos>": 256, "<eos>": 257, "<pad>": 258}) | |
| self.ids_to_tokens = {v: k for k, v in self.vocab.items()} | |
| self.zwnj_policy = zwnj_policy | |
| self.digit_policy = digit_policy | |
| self.remove_diacritics = remove_diacritics | |
| kwargs.pop("bos_token", None) | |
| kwargs.pop("eos_token", None) | |
| kwargs.pop("pad_token", None) | |
| kwargs.pop("unk_token", None) | |
| kwargs.pop("model_max_length", None) | |
| super().__init__( | |
| bos_token="<bos>", | |
| eos_token="<eos>", | |
| pad_token="<pad>", | |
| unk_token="<pad>", | |
| model_max_length=2048, | |
| **kwargs, | |
| ) | |
| def vocab_size(self): | |
| return 259 | |
| def get_vocab(self): | |
| return dict(self.vocab) | |
| def _tokenize(self, text: str) -> List[str]: | |
| return [f"byte_{b}" for b in text.encode("utf-8")] | |
| def _convert_token_to_id(self, token): | |
| return self.vocab.get(token, 258) | |
| def _convert_id_to_token(self, index): | |
| return self.ids_to_tokens.get(int(index), "<pad>") | |
| def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): | |
| if token_ids_1 is not None: | |
| token_ids_0 = token_ids_0 + token_ids_1 | |
| return [256] + list(token_ids_0) | |
| def save_vocabulary(self, save_directory, filename_prefix=None): | |
| path = Path(save_directory) / ((filename_prefix + "-" if filename_prefix else "") + "vocab.json") | |
| path.write_text(json.dumps(self.vocab, ensure_ascii=False, indent=2), encoding="utf-8") | |
| return (str(path),) | |
| def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=None, **kwargs): | |
| if hasattr(token_ids, "tolist"): | |
| token_ids = token_ids.tolist() | |
| if token_ids and isinstance(token_ids[0], list): | |
| token_ids = token_ids[0] | |
| bs = [] | |
| for i in token_ids: | |
| i = int(i) | |
| if 0 <= i < 256: | |
| bs.append(i) | |
| elif not skip_special_tokens and i in (256, 257, 258): | |
| continue | |
| return bytes(bs).decode("utf-8", errors="replace") | |