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@@ -235,3 +235,46 @@ Taking inspiration from [the Alpaca Repo](https://github.com/tatsu-lab/stanford_
 Comparing our model LoRa to the [Alpaca LoRa](https://huggingface.co/tloen/alpaca-lora-7b), our model has lower perplexity. Qualitatively, training on 3 epochs performed the best on perplexity as well as qualitative examples. 
 
 We tried training a full model using the parameters above, but found that during the second epoch the model diverged and samples generated post training were worse than the first epoch. 
+
+
+## GPT-J Training
+
+### Model Training Divergence
+
+We trained multiple [GPT-J models](https://huggingface.co/EleutherAI/gpt-j-6b) with varying success. We found that training the full model lead to diverged post epoch 1. ![](figs/overfit-gpt-j.png). We release the checkpoint after epoch 1.
+
+
+Using Atlas, we extracted the embeddings and calculated the per sequence level loss. We then uploaded [this to Atlas](https://atlas.nomic.ai/map/gpt4all-j-post-epoch-1-embeddings) and noticed that the higher loss items seem to cluster. On further inspection, the highest density clusters seemded to be of prompt/response pairs that asked for creative-like generations such as `Generate a story about ...` ![](figs/clustering_overfit.png)
+
+
+
+### GPT4All-J Hyperparameters
+
+We varied learning rate, learning rate schedule, and weight decay following suggestions from the [original GPT-J codebase](https://github.com/kingoflolz/mesh-transformer-jax/blob/master/howto_finetune.md) but found no real performance difference (qualitatively or quantitatively) when varying these parameters.
+
+
+
+The final model was trained using the following hyperparameters with a linear warmup followed by constant learning rate:
+
+| Hyperparameter | Value |
+|----------------|-------|
+| Per Device BS  | 32    |
+| Global BS      | 256   |
+| Learning rate  | 2e-5  |
+| Epochs         | 2     |
+| Max length     | 1024  |
+| Weight decay   | 0     |
+| Warmup Steps   | 500   |
+
+
+The LoRA model was trained using using the following hyperparameters with a linear warmup followed by constant learning rate: 
+
+| Hyperparameter | Value |
+|----------------|-------|
+| Per Device BS  | 4     |
+| Global BS      | 32    |
+| Learning rate  | 2e-5  |
+| Epochs         | 2     |
+| Max length     | 1024  |
+| Weight decay   | 0     |
+| Warmup Steps   | 500   |
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