metadata
language:
- en
tags:
- pytorch
- causal-lm
- bitnet
- layer-skipping
- safetensors
bitskip2
This is a fine-tuned BitNet model with layer skipping capabilities, converted to SafeTensors format for efficient loading.
Model Details
- Model Type: BitNet with Layer Skipping
- Base Model: Unknown
- Architecture: Unknown
- Format: SafeTensors (efficient loading)
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("USERNAME/MODEL_NAME")
tokenizer = AutoTokenizer.from_pretrained("USERNAME/MODEL_NAME")
# Generate text
inputs = tokenizer("Hello, how are you?", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Training
This model was trained using the LayerSkip framework with BitNet architecture and converted to SafeTensors format for efficient deployment.
Model Format
This model is saved in SafeTensors format, which provides:
- Faster loading times
- Memory safety
- Better error handling
- Cross-platform compatibility
License
[Add your license information here]