61 lines
2.5 KiB
Markdown
61 lines
2.5 KiB
Markdown
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---
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base_model: meta-llama/Llama-3.2-1B
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library_name: transformers
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license: llama3.2
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pipeline_tag: text-generation
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tags:
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- flashnorm
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- transformer-tricks
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- efficient-inference
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- weightless-rmsnorm
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---
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# Llama-3.2-1B-FlashNorm
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FlashNorm-prepared checkpoint of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B). This model was presented in the paper [FlashNorm: Fast Normalization for Transformers](https://huggingface.co/papers/2407.09577).
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Mathematically equivalent to the source model. 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.
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> **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.
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## What FlashNorm does
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An exact reformulation of `RMSNorm -> Linear`:
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- Fold the per-channel normalization weight `g` into the following linear layer: `W_star = W @ diag(g)`, computed once at checkpoint conversion.
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- After folding, the RMSNorm layer has no learnable per-channel scale. At runtime it simply divides by `rms(x)`.
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- The resulting model computes the same output as the original, by Proposition 1 of the FlashNorm paper.
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See the [paper](https://arxiv.org/abs/2407.09577) and the [transformer-tricks](https://github.com/OpenMachine-ai/transformer-tricks) repo for details.
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## Usage
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### Regenerate locally with `transformer_tricks`
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```python
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import transformer_tricks as tt
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tt.flashify_repo('meta-llama/Llama-3.2-1B', strict=True)
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```
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### Via HuggingFace Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tok = AutoTokenizer.from_pretrained('open-machine/Llama-3.2-1B-FlashNorm')
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model = AutoModelForCausalLM.from_pretrained('open-machine/Llama-3.2-1B-FlashNorm')
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ids = tok('Once upon a time', return_tensors='pt').input_ids
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out = model.generate(ids, max_new_tokens=50, do_sample=False)
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print(tok.decode(out[0], skip_special_tokens=True))
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```
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A warning about missing norm weights is expected; Transformers defaults those to ones, which is the correct value for a FlashNorm checkpoint.
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### Via vLLM
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Not yet supported. See the tracking issue linked above.
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## License
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Inherited from the source model.
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