Text Generation
Transformers
ONNX
Safetensors
English
ijk_byte_gpt
gpt
byte-tokenization
mobile
embedded
custom_code
Instructions to use ijktech/ByteGPT-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ijktech/ByteGPT-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ijktech/ByteGPT-small", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ijktech/ByteGPT-small", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ijktech/ByteGPT-small with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ijktech/ByteGPT-small" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ijktech/ByteGPT-small", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ijktech/ByteGPT-small
- SGLang
How to use ijktech/ByteGPT-small 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 "ijktech/ByteGPT-small" \ --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": "ijktech/ByteGPT-small", "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 "ijktech/ByteGPT-small" \ --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": "ijktech/ByteGPT-small", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ijktech/ByteGPT-small with Docker Model Runner:
docker model run hf.co/ijktech/ByteGPT-small
File size: 896 Bytes
d95f1d1 | 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 | from transformers import PretrainedConfig
class ByteGPTConfig(PretrainedConfig):
model_type = "ijk_byte_gpt"
def __init__(
self,
vocab_size: int = 259,
block_size: int = 128,
n_embd: int = 64,
n_head: int = 4,
n_layer: int = 4,
dropout: float = 0.1,
use_flash_attention: bool = False,
_attn_implementation_autoset: bool = False,
**kwargs
):
super().__init__(**kwargs)
self.auto_map = {
"AutoConfig": "configuration_bytegpt.ByteGPTConfig",
"AutoModelForCausalLM": "modeling_bytegpt.ByteGPTForCausalLM",
}
self.vocab_size = vocab_size
self.block_size = block_size
self.n_embd = n_embd
self.n_head = n_head
self.n_layer = n_layer
self.dropout = dropout
self.use_flash_attention = use_flash_attention
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