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Model: open-machine/SmolLM2-135M-FlashNorm Source: Original Platform
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README.md
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---
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license: apache-2.0
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base_model: HuggingFaceTB/SmolLM2-135M
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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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pipeline_tag: text-generation
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---
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# SmolLM2-135M-FlashNorm
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FlashNorm-prepared checkpoint of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M). 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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>
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> Two additional Llama-family verification checkpoints are published as [Llama-3.2-1B-FlashNorm-test](https://huggingface.co/open-machine/Llama-3.2-1B-FlashNorm-test) and [Llama-3.1-8B-FlashNorm-test](https://huggingface.co/open-machine/Llama-3.1-8B-FlashNorm-test). These retain the norm tensors as all-ones (compatibility layout) so they load in stock vLLM today and are intended for experimentation. They will be republished as weightless variants once vLLM's loader supports absent norm tensors.
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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://github.com/OpenMachine-ai/transformer-tricks/blob/main/tex/flashNorm.tex) (Section 3.1 and Proposition 1) and the [transformer-tricks](https://github.com/OpenMachine-ai/transformer-tricks) repo for details.
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## What's different from the source checkpoint
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| Tensor | Source | This FlashNorm checkpoint |
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|---|---|---|
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| `model.layers.*.input_layernorm.weight` | learned per-channel `g` | **absent** |
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| `model.layers.*.self_attn.{q,k,v}_proj.weight` | `W` | `W @ diag(g_input_layernorm)` |
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| `model.layers.*.post_attention_layernorm.weight` | learned per-channel `g` | **absent** |
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| `model.layers.*.mlp.{gate,up}_proj.weight` | `W` | `W @ diag(g_post_attention_layernorm)` |
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| `model.norm.weight` | learned per-channel `g` | **absent** |
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All dtype conventions match the source (`bfloat16`). Mathematical identity to the source holds by construction.
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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('HuggingFaceTB/SmolLM2-135M', 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/SmolLM2-135M-FlashNorm')
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model = AutoModelForCausalLM.from_pretrained('open-machine/SmolLM2-135M-FlashNorm')
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ids = tok('Once upon a time there was', 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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## Verification
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Under fp32 inference, greedy generation from this checkpoint is bit-identical to the source SmolLM2-135M model. Under fp16 inference the output is within benchmark noise (see the Quality table in Section 5 of the paper).
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## License
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Apache-2.0, inherited from the source model.
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config.json
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{
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"LlamaForCausalLM"
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