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
| """Logging setup shared by every entrypoint. | |
| Console handler is UTF-8 (so Kashmiri samples print on Windows cp1252 shells) | |
| and a per-run file handler mirrors everything to <run_dir>/train.log. | |
| """ | |
| from __future__ import annotations | |
| import logging | |
| import os | |
| import sys | |
| _CONFIGURED = False | |
| def get_logger(name: str = "ksbyte", run_dir: str | None = None, | |
| level: int = logging.INFO) -> logging.Logger: | |
| global _CONFIGURED | |
| logger = logging.getLogger(name) | |
| logger.setLevel(level) | |
| if not _CONFIGURED: | |
| fmt = logging.Formatter( | |
| "%(asctime)s | %(levelname)-7s | %(message)s", datefmt="%H:%M:%S" | |
| ) | |
| # Console: force UTF-8 so Arabic-script samples never crash the stream. | |
| stream = sys.stdout | |
| try: | |
| stream.reconfigure(encoding="utf-8") # py3.7+ | |
| except Exception: | |
| pass | |
| ch = logging.StreamHandler(stream) | |
| ch.setFormatter(fmt) | |
| logger.addHandler(ch) | |
| if run_dir: | |
| os.makedirs(run_dir, exist_ok=True) | |
| fh = logging.FileHandler(os.path.join(run_dir, "train.log"), encoding="utf-8") | |
| fh.setFormatter(fmt) | |
| logger.addHandler(fh) | |
| _CONFIGURED = True | |
| return logger | |