| --- |
| license: apache-2.0 |
| base_model: EleutherAI/pythia-160m-deduped |
| library_name: onnx |
| pipeline_tag: text-generation |
| tags: |
| - transformers.js |
| - onnx |
| - interpretability |
| - attention |
| - tuned-lens |
| --- |
| |
| # Pythia-160M Observable |
|
|
| Browser-oriented ONNX export of [`EleutherAI/pythia-160m-deduped`](https://huggingface.co/EleutherAI/pythia-160m-deduped) for the interactive laboratory in the Engenharia Assistida por IA course. |
|
|
| The repository contains two runtime artifacts: |
|
|
| - `model_observable_q4.onnx`, which returns final next-token logits, 13 residual-stream states, the full attention matrix and the selected query token's attention update for each of the 12 transformer layers; |
| - `model_tuned_lens_q8.onnx`, which applies the layer-specific translators from the pretrained [`AlignmentResearch/tuned-lens`](https://huggingface.co/spaces/AlignmentResearch/tuned-lens) artifact and decodes an intermediate state into vocabulary logits. |
|
|
| ## Observable model inputs |
|
|
| - `input_ids`: int64 tensor shaped `[batch, sequence]`; |
| - `attention_mask`: int64 tensor shaped `[batch, sequence]`; |
| - `query_index`: int64 tensor shaped `[batch]`. |
|
|
| The graph returns `next_token_logits`, `hidden_state_00` through `hidden_state_12`, `attention_01` through `attention_12`, and `attention_output_01` through `attention_output_12`. Each attention tensor has shape `[batch, heads, query sequence, key sequence]`. |
|
|
| ## Tuned lens inputs |
|
|
| - `hidden_states`: float32 tensor shaped `[states, 768]`; |
| - `layer_index`: int64 tensor shaped `[states]`, with values from 1 through 12. |
|
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| Layer 12 uses the model's final normalization and unembedding without a translator. Earlier layers use the pretrained translator for that residual-stream position. |
|
|
| ## Quantization |
|
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| Transformer matrix multiplications use weight-only Q4. The separate input and output embedding matrices use row-wise int8. The tuned lens translators and unembedding also use row-wise int8. Activations and public outputs remain float32. |
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| The export manifest records the numerical checks performed against the PyTorch model and the original tuned lens. Quantization can change close-ranking tokens, so this artifact is intended for teaching and inspection, not evaluation or production inference. |
|
|
| ## Limitations |
|
|
| - Pythia-160M is a small base model, not an instruction-following model. |
| - Attention weights describe values computed inside the model. They do not prove that a source token caused a prediction. |
| - Intermediate predictions are diagnostic readouts from a tuned lens, not text generated by stopping the original model early. |
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|
