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');
Upload observable-model.json with huggingface_hub
Browse files- observable-model.json +4 -3
observable-model.json
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{
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"model_id": "EleutherAI/pythia-160m-deduped",
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"model_file": "model_observable_q4.onnx",
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"model_bytes":
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"tuned_lens_file": "model_tuned_lens_q8.onnx",
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"tuned_lens_bytes": 46604627,
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"architecture": {
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"outputs": 38,
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"top_10_overlap": 8,
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"max_logit_error": 2.7454833984375,
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"max_attention_error":
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"max_attention_output_error": 0.34226417541503906,
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"max_cumulative_mean_attention_error": 0.
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"max_attention_sum_error": 2.384185791015625e-07,
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"logits_shape": [
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"attention_shape": [
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],
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"attention_output_shape": [
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{
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"model_id": "EleutherAI/pythia-160m-deduped",
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"model_file": "model_observable_q4.onnx",
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"model_bytes": 133860438,
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"tuned_lens_file": "model_tuned_lens_q8.onnx",
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"tuned_lens_bytes": 46604627,
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"architecture": {
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"outputs": 38,
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"top_10_overlap": 8,
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"max_logit_error": 2.7454833984375,
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"max_attention_error": 1.0,
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"max_attention_output_error": 0.34226417541503906,
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"max_cumulative_mean_attention_error": 0.041846275329589844,
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"max_attention_sum_error": 2.384185791015625e-07,
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"logits_shape": [
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1,
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"attention_shape": [
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10,
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],
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"attention_output_shape": [
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