Pythia-160M Observable
Browser-oriented ONNX export of
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 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.
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
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.
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.
