Add evaluations

README.md

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