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pythia-delphi-micromachine-funnel
jtatman/pythia-delphi-micromachine-funnel
pythia-delphi-micromachine-funnel is a text generation model from jtatman. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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At a glance
- Task
- Text Generation
- Library
- transformers
- License
- apache-2.0
- Model type
- gpt_neox
- Access
- Public
- Created
- Jun 28, 2024
- Updated
- Jun 29, 2024
- SHA
- 96aea78b
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Model card
From the Hugging Face model README
Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->Model Details
Model Description
<!-- Provide a longer summary of what this model is. -->This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- Developed by: Tatman Electric
- Funded by [optional]: Spare Pocket Lint
- Shared by [optional]: TRL
- Model type: Sliced Layered
- Language(s) (NLP): Mixed
- License: Pythia @ EleutherAI
- Finetuned from model [optional]: EleutherAI/pythia-2.8b-deduped
Model Sources [optional]
<!-- Provide the basic links for the model. -->- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
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Direct Use
Part of a series of slice and dice mods.
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->Single Hidden Layer Pythia
What does a single hidden layer preserve from a 12 layer base model?
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Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
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How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
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Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->Preprocessing [optional]
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Training Hyperparameters
- Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->[More Information Needed]
Evaluation
| Groups | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| Open LLM Leaderboard | N/A | none | 5 | rouge1_max | 36.3550 | ± | 0.9462 |
| flexible-extract | 5 | exact_match | 0.0220 | ± | 0.0066 | ||
| - arc_challenge | 1 | none | 25 | acc | 0.1760 | ± | 0.0170 |
| none | 25 | acc_norm | 0.2320 | ± | 0.0189 | ||
| - gsm8k | 3 | strict-match | 5 | exact_match | 0.0060 | ± | 0.0035 |
| flexible-extract | 5 | exact_match | 0.0220 | ± | 0.0066 | ||
| - hellaswag | 1 | none | 10 | acc | 0.3520 | ± | 0.0214 |
| none | 10 | acc_norm | 0.4040 | ± | 0.0220 | ||
| - winogrande | 1 | none | 5 | acc | 0.5120 | ± | 0.0224 |
| none | 5 | bleu_diff | -0.6500 | ± | 0.6421 | ||
| none | 5 | rouge1_acc | 0.3700 | ± | 0.0216 | ||
| none | 5 | rouge1_diff | -1.5564 | ± | 1.0223 | ||
| none | 5 | acc | 0.2664 | ± | 0.0036 | ||
| none | 5 | rougeL_max | 33.8798 | ± | 0.9367 | ||
| none | 5 | rouge2_diff | -3.3178 | ± | 0.9477 | ||
| none | 5 | bleu_max | 15.2292 | ± | 0.6714 | ||
| none | 5 | bleu_acc | 0.4360 | ± | 0.0222 | ||
| none | 5 | rouge2_max | 16.4873 | ± | 1.0172 | ||
| none | 5 | acc_norm | 0.3180 | ± | 0.0145 | ||
| strict-match | 5 | exact_match | 0.0060 | ± | 0.0035 | ||
| none | 5 | rougeL_diff | -0.7765 | ± | 1.0034 | ||
| none | 5 | rougeL_acc | 0.3860 | ± | 0.0218 | ||
| none | 5 | rouge2_acc | 0.1920 | ± | 0.0176 | ||
| - mmlu | N/A | none | 0 | acc | 0.2533 | ± | 0.0039 |
| - humanities | N/A | none | 5 | acc | 0.2408 | ± | 0.0075 |
| - other | N/A | none | 5 | acc | 0.2443 | ± | 0.0080 |
| - social_sciences | N/A | none | 5 | acc | 0.2538 | ± | 0.0081 |
| - stem | N/A | none | 5 | acc | 0.2740 | ± | 0.0079 |
| - truthfulqa | N/A | none | 0 | rouge1_max | 36.3550 | ± | 0.9462 |
| none | 0 | bleu_diff | -0.6500 | ± | 0.6421 | ||
| none | 0 | rouge1_acc | 0.3700 | ± | 0.0216 | ||
| none | 0 | rouge1_diff | -1.5564 | ± | 1.0223 | ||
| none | 0 | acc | 0.3435 | ± | 0.0137 | ||
| none | 0 | rougeL_max | 33.8798 | ± | 0.9367 | ||
| none | 0 | bleu_max | 15.2292 | ± | 0.6714 | ||
| none | 0 | bleu_acc | 0.4360 | ± | 0.0222 | ||
| none | 0 | rouge2_max | 16.4873 | ± | 1.0172 | ||
| none | 0 | rougeL_acc | 0.3860 | ± | 0.0218 | ||
| none | 0 | rougeL_diff | -0.7765 | ± | 1.0034 | ||
| none | 0 | rouge2_acc | 0.1920 | ± | 0.0176 | ||
| none | 0 | rouge2_diff | -3.3178 | ± | 0.9477 |
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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Results
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Summary
Model Examination [optional]
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Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: OldAsDirt
- Hours used: 5
- Cloud Provider: YourMomsBasement
- Compute Region: Siberia
- Carbon Emitted: 8ppm
No yaks were harmed in the making of this model.
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
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Hardware
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Software
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Citation [optional]
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APA:
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Glossary [optional]
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Model Card Authors [optional]
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- Task
- Text Generation
- Library
- transformers
- Type
- gpt_neox
- License
- apache-2.0
- Created
- Jun 28, 2024
- Updated
- Jun 29, 2024