Instructions to use diegoquinteiro/Pythia-160M-Observable with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use diegoquinteiro/Pythia-160M-Observable with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-generation', 'diegoquinteiro/Pythia-160M-Observable');
| { | |
| "model_id": "EleutherAI/pythia-160m-deduped", | |
| "model_file": "model_observable_q4.onnx", | |
| "model_bytes": 133860438, | |
| "tuned_lens_file": "model_tuned_lens_q8.onnx", | |
| "tuned_lens_bytes": 46604627, | |
| "architecture": { | |
| "layers": 12, | |
| "attention_heads": 12, | |
| "hidden_size": 768, | |
| "vocabulary_size": 50304 | |
| }, | |
| "inputs": [ | |
| "input_ids", | |
| "attention_mask", | |
| "query_index" | |
| ], | |
| "outputs": [ | |
| "next_token_logits", | |
| "hidden_state_00", | |
| "hidden_state_01", | |
| "hidden_state_02", | |
| "hidden_state_03", | |
| "hidden_state_04", | |
| "hidden_state_05", | |
| "hidden_state_06", | |
| "hidden_state_07", | |
| "hidden_state_08", | |
| "hidden_state_09", | |
| "hidden_state_10", | |
| "hidden_state_11", | |
| "hidden_state_12", | |
| "attention_01", | |
| "attention_02", | |
| "attention_03", | |
| "attention_04", | |
| "attention_05", | |
| "attention_06", | |
| "attention_07", | |
| "attention_08", | |
| "attention_09", | |
| "attention_10", | |
| "attention_11", | |
| "attention_12", | |
| "attention_output_01", | |
| "attention_output_02", | |
| "attention_output_03", | |
| "attention_output_04", | |
| "attention_output_05", | |
| "attention_output_06", | |
| "attention_output_07", | |
| "attention_output_08", | |
| "attention_output_09", | |
| "attention_output_10", | |
| "attention_output_11", | |
| "attention_output_12" | |
| ], | |
| "validation_prompt": "The mayor supports the bike lane. Her opinion is", | |
| "observable_validation": { | |
| "outputs": 38, | |
| "top_10_overlap": 8, | |
| "max_logit_error": 2.7454833984375, | |
| "max_attention_error": 1.0, | |
| "max_attention_output_error": 0.34226417541503906, | |
| "max_cumulative_mean_attention_error": 0.041846275329589844, | |
| "max_attention_sum_error": 2.384185791015625e-07, | |
| "logits_shape": [ | |
| 1, | |
| 50304 | |
| ], | |
| "hidden_shape": [ | |
| 1, | |
| 10, | |
| 768 | |
| ], | |
| "attention_shape": [ | |
| 1, | |
| 12, | |
| 10, | |
| 10 | |
| ], | |
| "attention_output_shape": [ | |
| 1, | |
| 768 | |
| ] | |
| }, | |
| "tuned_lens_validation": { | |
| "layers": 12, | |
| "top_10_overlap_min": 8, | |
| "top_10_overlap_mean": 9.666666666666666, | |
| "max_logit_error": 0.55035400390625, | |
| "top_token_ids": [ | |
| 247, | |
| 247, | |
| 417, | |
| 247, | |
| 247, | |
| 417, | |
| 326, | |
| 1754, | |
| 1754, | |
| 1754, | |
| 1754, | |
| 326 | |
| ], | |
| "logits_shape": [ | |
| 1, | |
| 50304 | |
| ] | |
| }, | |
| "tuned_lens_source": "AlignmentResearch/tuned-lens" | |
| } | |