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Add evaluations

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  1. README.md +39 -19
README.md CHANGED
@@ -21,13 +21,13 @@ same data, in the exact same order. All Pythia models are available
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  The Pythia model suite was deliberately designed to promote scientific
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  research on large language models, especially interpretability research.
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  Despite not centering downstream performance as a design goal, we find the
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- models match or exceed the performance of similar and same-sized models,
25
- such as those in the OPT and GPT-Neo suites.
26
 
27
  Please note that all models in the *Pythia* suite were renamed in January
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  2023. For clarity, a <a href="#naming-convention-and-parameter-count">table
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  comparing the old and new names</a> is provided in this model card, together
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- with exact model parameter counts.
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  ## Pythia-1.4B-deduped
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@@ -143,8 +143,7 @@ tokenizer.decode(tokens[0])
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  ```
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  Revision/branch `step143000` corresponds exactly to the model checkpoint on
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- the `main` branch of each model.
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-
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  For more information on how to use all Pythia models, see [documentation on
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  GitHub](https://github.com/EleutherAI/pythia).
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@@ -153,8 +152,7 @@ GitHub](https://github.com/EleutherAI/pythia).
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  #### Training data
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  Pythia-1.4B-deduped was trained on the Pile **after the dataset has been
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- globally deduplicated**.
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-
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  [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in
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  English. It was created by EleutherAI specifically for training large language
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  models. It contains texts from 22 diverse sources, roughly broken down into
@@ -170,9 +168,6 @@ mirror](https://the-eye.eu/public/AI/pile/).
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  #### Training procedure
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- Pythia uses the same tokenizer as [GPT-NeoX-
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- 20B](https://huggingface.co/EleutherAI/gpt-neox-20b).
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-
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  All models were trained on the exact same data, in the exact same order. Each
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  model saw 299,892,736,000 tokens during training, and 143 checkpoints for each
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  model are saved every 2,097,152,000 tokens, spaced evenly throughout training.
@@ -186,21 +181,46 @@ checkpoints every 500 steps. The checkpoints on Hugging Face are renamed for
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  consistency with all 2M batch models, so `step1000` is the first checkpoint
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  for `pythia-1.4b` that was saved (corresponding to step 500 in training), and
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  `step1000` is likewise the first `pythia-6.9b` checkpoint that was saved
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- (corresponding to 1000 “actual” steps).
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-
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- See [GitHub](https://github.com/EleutherAI/pythia) for more details on training
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- procedure, including [how to reproduce
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- it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).
 
194
 
195
  ### Evaluations
196
 
197
  All 16 *Pythia* models were evaluated using the [LM Evaluation
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  Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access
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  the results by model and step at `results/json/*` in the [GitHub
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- repository](https://github.com/EleutherAI/pythia/tree/main/results/json).
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-
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- February 2023 note: select evaluations and comparison with OPT and BLOOM
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- models will be added here at a later date.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Naming convention and parameter count
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21
  The Pythia model suite was deliberately designed to promote scientific
22
  research on large language models, especially interpretability research.
23
  Despite not centering downstream performance as a design goal, we find the
24
+ models <a href="#evaluations">match or exceed</a> the performance of
25
+ similar and same-sized models, such as those in the OPT and GPT-Neo suites.
26
 
27
  Please note that all models in the *Pythia* suite were renamed in January
28
  2023. For clarity, a <a href="#naming-convention-and-parameter-count">table
29
  comparing the old and new names</a> is provided in this model card, together
30
+ with exact parameter counts.
31
 
32
  ## Pythia-1.4B-deduped
33
 
 
143
  ```
144
 
145
  Revision/branch `step143000` corresponds exactly to the model checkpoint on
146
+ the `main` branch of each model.<br>
 
147
  For more information on how to use all Pythia models, see [documentation on
148
  GitHub](https://github.com/EleutherAI/pythia).
149
 
 
152
  #### Training data
153
 
154
  Pythia-1.4B-deduped was trained on the Pile **after the dataset has been
155
+ globally deduplicated**.<br>
 
156
  [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in
157
  English. It was created by EleutherAI specifically for training large language
158
  models. It contains texts from 22 diverse sources, roughly broken down into
 
168
 
169
  #### Training procedure
170
 
 
 
 
171
  All models were trained on the exact same data, in the exact same order. Each
172
  model saw 299,892,736,000 tokens during training, and 143 checkpoints for each
173
  model are saved every 2,097,152,000 tokens, spaced evenly throughout training.
 
181
  consistency with all 2M batch models, so `step1000` is the first checkpoint
182
  for `pythia-1.4b` that was saved (corresponding to step 500 in training), and
183
  `step1000` is likewise the first `pythia-6.9b` checkpoint that was saved
184
+ (corresponding to 1000 “actual” steps).<br>
185
+ See [GitHub](https://github.com/EleutherAI/pythia) for more details on training
186
+ procedure, including [how to reproduce
187
+ it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br>
188
+ Pythia uses the same tokenizer as [GPT-NeoX-
189
+ 20B](https://huggingface.co/EleutherAI/gpt-neox-20b).
190
 
191
  ### Evaluations
192
 
193
  All 16 *Pythia* models were evaluated using the [LM Evaluation
194
  Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access
195
  the results by model and step at `results/json/*` in the [GitHub
196
+ repository](https://github.com/EleutherAI/pythia/tree/main/results/json).<br>
197
+ Expand the sections below to see plots of evaluation results for all
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+ Pythia and Pythia-deduped models compared with OPT and BLOOM.
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+
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+ <details>
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+ <summary>LAMBADA – OpenAI</summary>
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+ <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai.png" style="width:auto"/>
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+ </details>
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+
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+ <details>
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+ <summary>Physical Interaction: Question Answering (PIQA)</summary>
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+ <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa.png" style="width:auto"/>
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+ </details>
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+
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+ <details>
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+ <summary>WinoGrande</summary>
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+ <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande.png" style="width:auto"/>
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+ </details>
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+
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+ <details>
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+ <summary>AI2 Reasoning Challenge – Challenge Set</summary>
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+ <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_challenge.png" style="width:auto"/>
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+ </details>
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+
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+ <details>
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+ <summary>SciQ</summary>
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+ <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq.png" style="width:auto"/>
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+ </details>
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  ### Naming convention and parameter count
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