TinyLlama-1.1B-Chat-v1.0-Unfiltered

Model Card: TinyLlama-1.1B-Chat-v1.0-Unfiltered

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Model Name: TinyLlama-1.1B-Chat-v1.0-Unfiltered
Model Type: Conversational AI Model
Architecture: Based on a 1.1B parameter TinyLlama architecture

Training Data:

• Fine-tuned on the "dan_remixed" dataset (2.7MB).
• The dataset improves spelling, grammar, and consistency while replacing references to violent crimes with non-violent activities and removes self-censorship from explicatives.

Training Time: Approximately 30-45 minutes. Each validation epoch takes ~322 seconds.
Hardware: Trained on Google Colab Pro A100 GPU (40GB).

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Training Performance:

• Epoch Losses:
  • Epoch 1: 0.7209
  • Epoch 2: 0.4441
  • Epoch 3: 0.3683
  • Epoch 4: 0.3358
  • Epoch 5: 0.3145
• Final Training Loss (Epoch 5): 0.3145

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Validation Performance (5 Epochs):

• Epoch 1:

  • Training Loss: 0.2921
  • Validation Loss: 0.7962
  • Perplexity: 2.22
  • Epoch completed in 321.64 seconds

• Epoch 2:

  • Training Loss: 0.2872
  • Validation Loss: 0.7672
  • Perplexity: 2.15
  • Epoch completed in 321.91 seconds

• Epoch 3:

  • Training Loss: 0.2874
  • Validation Loss: 0.7821
  • Perplexity: 2.19
  • Epoch completed in 321.94 seconds

• Epoch 4:

  • Training Loss: 0.2864
  • Validation Loss: 0.7796
  • Perplexity: 2.18
  • Epoch completed in 322.01 seconds

Epoch 5:

• Training Loss: 0.2831
• Validation Loss: 0.8017
• Perplexity: 2.23
• Epoch completed in 322.01 seconds

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Optimizer: AdamW, learning rate: 1e-5
Loss Function: Cross-Entropy Loss, ignoring padding tokens (ignore_index=-100)
Use Case: Conversational AI designed for general, unrestricted conversation, with no filtering on the nature of responses, provided the content is non-violent.

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Limitations:

• Due to the small fine-tuning dataset size (2.7MB), the model may be prone to overfitting and bias.
• The dataset has been modified to avoid violent language, but the model might still exhibit strong or explicit responses.

Metrics:

• Loss and perplexity have been tracked, and more conversational metrics (like BLEU, ROUGE, or human evaluation) could be explored.