End of training

README.md

@@ -12,7 +12,37 @@ should probably proofread and complete it, then remove this comment. -->
 
 # ProGemma
 
-This is a custom configuration (275M parameters) of a Gemma 2 LLM that is being pre-trained on a corpus of amino acid sequences with the goal of generating de novo amino acid sequences in a zero-shot fashion. The model has currently been trained on 55% of the dataset as of 8.14.2024. A new version of the model will be continually pushed as training hits new checkpoints. Preliminary evaluation demonstrates perplexity scores, HHblit E-values, pLDDT, pTM, and ipTM scores on par with ProtGPT2. A full evaluation will be done upon completion of training. This model can be easily accessed using the transformers.pipeline function. For the tokenizer, use "JuIm/Amino-Acid-Sequence-Tokenizer".  
+This model is a fine-tuned version of [JuIm/ProGemma](https://huggingface.co/JuIm/ProGemma) on an unknown dataset.
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.001
+- train_batch_size: 1
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.4
+- training_steps: 7000
+
+### Training results
+
+
 
 ### Framework versions
 

model.safetensors

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runs/Aug15_18-47-15_084ff3810925/events.out.tfevents.1723747641.084ff3810925.1011.0

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training_args.bin

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