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--- |
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library_name: transformers |
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tags: |
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- function calling |
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- laser |
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license: apache-2.0 |
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datasets: |
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- jtatman/glaive_function_calling_v2_filtered_10k |
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--- |
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# Model Card |
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This is a laser fine tuning of Aloobun's [great 1.5b param reyna mini model](https://huggingface.co/aloobun/Reyna-Mini-1.8B-v0.2). |
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### Model Description |
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This model is quite conversational - even a bit more so after laser tuning even using Peft. The function calling is mediocre, but will be improved in future versions. |
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## Uses |
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As Aloobun's model is well performing and impressive on it's own, I decided to add some function calling while practicing the LaserRMT technique. |
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### Direct Use |
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Chat |
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Conversational |
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Text Generation |
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Function Calling |
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## Bias, Risks, and Limitations |
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This model will take over your house, borrow your car, talk badly to your family, and generally make everything incrementally worse. If you use it for nefarious purposes. |
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### Recommendations |
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Use at your own risk. It's a great small model, owing to the base model before tuning. |
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## Training Details |
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### Training Data |
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- "eval/loss": 2.1797242164611816, |
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- "_timestamp": 1708624900.2239263, |
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- "_runtime": 20945.370138406754, |
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- "train/train_loss": 2.515587423102269, |
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- "train/global_step": 918, |
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- "train/train_steps_per_second": 0.044, |
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- "train/loss": 2.2062, |
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- "train/learning_rate": 0, |
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- "train/train_samples_per_second": 1.403, |
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- "train/train_runtime": 20945.6359, |
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- "eval/steps_per_second": 4.867, |
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- "eval/samples_per_second": 4.867, |
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- "_step": 923, |
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- "train/epoch": 2.98, |
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- "eval/runtime": 41.0972, |
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- "train/grad_norm": 0.2638521194458008, |
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- "train/total_flos": 141790931224363000 |
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### Training Procedure |
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[LaserRMT](https://github.com/cognitivecomputations/laserRMT) was used to refine the weights, using the 16 highest scored weights specifically by noise-to-ratio analysis. |
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This technique avoids training unnecessarily low-performng weights that can turn to garbage. By pruning these weights, the model size is decreased slightly. |
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Axolotl was used for training and dataset tokenization. |
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#### Preprocessing [optional] |
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Dataset was formatted in ShareGpt format for the purposes of using with Axolotl, in conversational format. |
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#### Training Hyperparameters |
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lora_r: 64 |
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lora_alpha: 16 |
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lora_dropout: 0.05 |
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gradient_accumulation_steps: 4 |
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micro_batch_size: 1 |
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num_epochs: 3 |
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optimizer: adamw_bnb_8bit |
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lr_scheduler: cosine |
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learning_rate: 0.00025 |
