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Model: open-machine/Llama-3.1-8B-FlashNorm
Source: Original Platform

README.md

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+---
+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}, 
+}
+```