---
library_name: diffusers
tags:
- fp8
- safetensors
- lora
- low-rank
- diffusion
- converted-by-gradio
---
# FP8 Model with Low-Rank LoRA
- **Source**: `https://huggingface.co/LifuWang/DistillT5`
- **File**: `model.safetensors`
- **FP8 Format**: `E5M2`
- **LoRA Rank**: 128
- **Architecture**: text_encoder
- **LoRA File**: `model-lora-r128.safetensors`
- **FP8 File**: `model-fp8-e5m2.safetensors`
## Usage (Inference)
```python
from safetensors.torch import load_file
import torch
# Load FP8 model
fp8_state = load_file("model-fp8-e5m2.safetensors")
lora_state = load_file("model-lora-r128.safetensors")
# Reconstruct approximate original weights
reconstructed = {}
for key in fp8_state:
    if f"lora_A.{key}" in lora_state and f"lora_B.{key}" in lora_state:
        A = lora_state[f"lora_A.{key}"].to(torch.float32)
        B = lora_state[f"lora_B.{key}"].to(torch.float32)
        lora_weight = B @ A  # (out_features, rank) @ (rank, in_features) -> (out_features, in_features)
        fp8_weight = fp8_state[key].to(torch.float32)
        reconstructed[key] = fp8_weight + lora_weight
    else:
        reconstructed[key] = fp8_state[key].to(torch.float32)
```
> Requires PyTorch ≥ 2.1 for FP8 support.