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]