Ram07 / bitnet-8bit-v2

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Model's Last Updated: July 28 2025

Introduction of bitnet-8bit-v2

Model Details of bitnet-8bit-v2

bitnet-8bit-v2

This is a BitNet model with 8-bit quantization, layer skipping, and early exit capabilities, trained on the FineWeb-EDU dataset.

Architecture Overview
Input Processing
  • Token Embeddings : 128,256 vocabulary size
  • Position Embeddings : Up to 128 positions
  • Hidden Dimensions : 1024-dimensional hidden states
Transformer Layers (12 total)

Each layer contains:

  • Layer normalization (eps=1e-05)
  • Multi-Head Attention : 16 heads
  • Residual connections
  • Feed-Forward Network : 1024 → 4096 → 1024
  • Dropout (0.1) after attention and FFN
  • Activation function: silu
Special Features
  • 8-bit Quantization : 8-bit activations for efficiency
  • Layer Skipping : Dynamic computation with skip probability 0.1
    • Minimum layers to keep: 4
  • Early Exit : Can terminate at any layer if confidence > 95%
Model Configuration
{
  "vocab_size": 128256,
  "hidden_size": 1024,
  "num_hidden_layers": 12,
  "num_attention_heads": 16,
  "intermediate_size": 4096,
  "max_position_embeddings": 128,
  "activation_bits": 8,
  "hidden_dropout_prob": 0.1,
  "attention_probs_dropout_prob": 0.1
}
Training Details
  • Dataset : FineWeb-EDU (sample-10BT subset)
  • Batch Size : 64 (with gradient accumulation)
  • Learning Rate : 5e-05
  • Weight Decay : 0.01
  • Warmup Steps : 1000
  • Max Gradient Norm : 1.0
  • Gradient Accumulation Steps : 4
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("bitnet-8bit-v2")
tokenizer = AutoTokenizer.from_pretrained("bitnet-8bit-v2")

# Basic generation
inputs = tokenizer("The key to understanding BitNet is", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Performance Characteristics
  • Memory Efficiency : 8-bit quantization reduces memory footprint
  • Adaptive Computation : Layer skipping reduces average computation by ~10%
  • Low Latency : Early exit can terminate computation when confident
  • Compact Size : Significantly smaller than full-precision models
Limitations
  • Maximum sequence length is limited to 128 tokens
  • This is an experimental BitNet implementation
  • Early exit and layer skipping require compatible inference code
  • Model performance may vary based on the complexity of the input
Technical Details
  • Initializer Range : 0.02
  • Layer Norm Epsilon : 1e-05
  • Tokenizer : Based on Meta-Llama-3-8B-Instruct tokenizer
  • Format : SafeTensors for fast and safe loading
Citation

If you use this model, please cite:

@misc{bitnet2024,
  title={BitNet: 8-bit Quantized Transformer with Layer Skipping},
  author={Your Name},
  year={2024},
  url={https://huggingface.co/bitnet-8bit-v2}
}
License

Apache 2.0 - This model can be used for commercial purposes.

Acknowledgments
  • Training data from FineWeb-EDU by HuggingFace
  • Tokenizer from Meta's Llama-3-8B-Instruct model

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