---
base_model: meta-llama/Llama-3.1-8B
library_name: transformers
license: llama3.1
pipeline_tag: text-generation
tags:
- flashnorm
- transformer-tricks
- efficient-inference
- weightless-rmsnorm
---

# Llama-3.1-8B-FlashNorm

FlashNorm-prepared checkpoint of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B). Mathematically equivalent to the source model. This model was presented in the paper [FlashNorm: Fast Normalization for Transformers](https://huggingface.co/papers/2407.09577).

The per-channel RMSNorm weight tensors (`input_layernorm.weight`, `post_attention_layernorm.weight`, `model.norm.weight`) are folded into the following linear layers and then removed from the state dict entirely.

> **Framework support note.** Stock vLLM currently does not load this checkpoint because the norm weight tensors are absent. The upstream patch to accept missing tensors is tracked at: **TBD (vLLM issue link)**. Until the patch lands, use HuggingFace Transformers; it loads this with a warning that norm weights were not initialized and defaults them to ones, which is the correct behavior for FlashNorm.

## What FlashNorm does

An exact reformulation of `RMSNorm -> Linear`:

- Fold the per-channel normalization weight `g` into the following linear layer: `W_star = W @ diag(g)`, computed once at checkpoint conversion.
- After folding, the RMSNorm layer has no learnable per-channel scale. At runtime it simply divides by `rms(x)`.
- The resulting model computes the same output as the original, by Proposition 1 of the FlashNorm paper.

See the [paper](https://arxiv.org/abs/2407.09577) and the [transformer-tricks](https://github.com/OpenMachine-ai/transformer-tricks) repo for details.

## Usage

### Regenerate locally with `transformer_tricks`

```python
import transformer_tricks as tt
tt.flashify_repo('meta-llama/Llama-3.1-8B', strict=True)
```

### Via HuggingFace Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained('open-machine/Llama-3.1-8B-FlashNorm')
model = AutoModelForCausalLM.from_pretrained('open-machine/Llama-3.1-8B-FlashNorm')

ids = tok('Once upon a time', return_tensors='pt').input_ids
out = model.generate(ids, max_new_tokens=50, do_sample=False)
print(tok.decode(out[0], skip_special_tokens=True))
```

A warning about missing norm weights is expected; Transformers defaults those to ones, which is the correct value for a FlashNorm checkpoint.

### Via vLLM

Not yet supported. See the tracking issue linked above.

## License

Inherited from the source model.

## Citation

```bibtex
@misc{graef2024flashnormfastnormalizationtransformers,
      title={FlashNorm: Fast Normalization for Transformers}, 
      author={Nils Graef and Matthew Clapp and Andrew Wasielewski},
      year={2024},
      eprint={2407.09577},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2407.09577}, 
}
```