DFloat11 / Qwen3-14B-DF11

DFloat11 Compressed Model: Qwen/Qwen3-14B

This is a losslessly compressed version of Qwen/Qwen3-14B using our custom DFloat11 format. The outputs of this compressed model are bit-for-bit identical to the original BFloat16 model, while reducing GPU memory consumption by approximately 30% .

🔍 How It Works

DFloat11 compresses model weights using Huffman coding of BFloat16 exponent bits, combined with hardware-aware algorithmic designs that enable efficient on-the-fly decompression directly on the GPU. During inference, the weights remain compressed in GPU memory and are decompressed just before matrix multiplications , then immediately discarded after use to minimize memory footprint.

Key benefits:

• No CPU decompression or host-device data transfer -- all operations are handled entirely on the GPU.
• Decompression overhead is constant per forward pass and independent of batch size , making DFloat11 increasingly efficient at larger batch sizes.
• DFloat11 is much faster than CPU-offloading approaches , enabling practical deployment in memory-constrained environments.
• At batch size = 1 , inference is approximately 2× slower than the original BF16 model, but the performance gap narrows significantly with larger batches.
• The compression is fully lossless , guaranteeing that the model’s outputs are bit-for-bit identical to those of the original model.

🔧 How to Use

1. Install the DFloat11 pip package (installs the CUDA kernel automatically; requires a CUDA-compatible GPU and PyTorch installed) :

   pip install dfloat11[cuda12]
   # or if you have CUDA version 11:
   # pip install dfloat11[cuda11]
   

2. To use the DFloat11 model, run the following example code in Python:

   import torch
   from dfloat11 import DFloat11Model
   from transformers import AutoTokenizer
   
   model_id = "DFloat11/Qwen3-14B-DF11"
   
   model = DFloat11Model.from_pretrained(model_id, device_map="auto")
   
   tokenizer = AutoTokenizer.from_pretrained(model_id)
   tokenizer.pad_token = tokenizer.eos_token
   
   prompt = "Question: What is a binary tree and its applications? Answer:"
   inputs = tokenizer(prompt, return_tensors="pt", padding=True).to(model.device)
   
   with torch.no_grad():
       output = model.generate(
           **inputs,
           max_new_tokens=256,
           do_sample=True,
       )
   
   print(tokenizer.batch_decode(output, skip_special_tokens=True))
   

📄 Learn More

• Paper : 70% Size, 100% Accuracy: Lossless LLM Compression for Efficient GPU Inference via Dynamic-Length Float
• GitHub : https://github.com/LeanModels/DFloat11
• HuggingFace : https://huggingface.co/DFloat11