Text Generation
Transformers
Safetensors
French
English
binaryllm
binary-level
bit-level
causal-lm
tokenizer-free
base2
binary
TinyTransformerLM
custom_code
Instructions to use PhysiQuanty/Binary-LLM-POC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PhysiQuanty/Binary-LLM-POC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PhysiQuanty/Binary-LLM-POC", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("PhysiQuanty/Binary-LLM-POC", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use PhysiQuanty/Binary-LLM-POC with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PhysiQuanty/Binary-LLM-POC" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PhysiQuanty/Binary-LLM-POC", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/PhysiQuanty/Binary-LLM-POC
- SGLang
How to use PhysiQuanty/Binary-LLM-POC 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 "PhysiQuanty/Binary-LLM-POC" \ --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": "PhysiQuanty/Binary-LLM-POC", "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 "PhysiQuanty/Binary-LLM-POC" \ --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": "PhysiQuanty/Binary-LLM-POC", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use PhysiQuanty/Binary-LLM-POC with Docker Model Runner:
docker model run hf.co/PhysiQuanty/Binary-LLM-POC
File size: 896 Bytes
d0ecd4a | 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 | from transformers import PretrainedConfig
class BinaryLLMConfig(PretrainedConfig):
model_type = "binaryllm"
def __init__(
self,
vocab_size: int = 4,
hidden_size: int = 384,
num_hidden_layers: int = 6,
num_attention_heads: int = 6,
intermediate_size: int = 1536,
max_position_embeddings: int = 4096,
dropout: float = 0.1,
activation: str = "gelu",
**kwargs,
):
self.vocab_size = int(vocab_size)
self.hidden_size = int(hidden_size)
self.num_hidden_layers = int(num_hidden_layers)
self.num_attention_heads = int(num_attention_heads)
self.intermediate_size = int(intermediate_size)
self.max_position_embeddings = int(max_position_embeddings)
self.dropout = float(dropout)
self.activation = str(activation)
super().__init__(**kwargs)
|