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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 |
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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 |
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#### Evaluation |
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| Groups |Version| Filter |n-shot| Metric | Value | |Stderr| |
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|--------------------|-------|----------------|-----:|-----------|------:|---|-----:| |
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|Open LLM Leaderboard|N/A |none | 5|rouge2_acc | 0.1920|± |0.0176| |
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| | |none | 5|bleu_max |15.2292|± |0.6714| |
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| | |flexible-extract| 5|exact_match| 0.0220|± |0.0066| |
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| - truthfulqa_mc1 | 2|none | 0|acc | 0.2440|± |0.0192| |
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| - truthfulqa_mc2 | 2|none | 0|acc | 0.4430|± |0.0195| |
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| - winogrande | 1|none | 5|acc | 0.5120|± |0.0224| |
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| - arc_challenge | 1|none | 25|acc | 0.1760|± |0.0170| |
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| | |none | 25|acc_norm | 0.2320|± |0.0189| |
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| - gsm8k | 3|strict-match | 5|exact_match| 0.0060|± |0.0035| |
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| | |flexible-extract| 5|exact_match| 0.0220|± |0.0066| |
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| - hellaswag | 1|none | 10|acc | 0.3520|± |0.0214| |
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| | |none | 10|acc_norm | 0.4040|± |0.0220| |
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| | |none | 5|rouge2_diff|-3.3178|± |0.9477| |
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| | |none | 5|rougeL_acc | 0.3860|± |0.0218| |
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| | |none | 5|acc_norm | 0.3180|± |0.0145| |
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| | |none | 5|rouge1_diff|-1.5564|± |1.0223| |
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| | |none | 5|bleu_diff |-0.6500|± |0.6421| |
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| | |none | 5|rouge2_max |16.4873|± |1.0172| |
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| | |none | 5|rougeL_diff|-0.7765|± |1.0034| |
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| | |strict-match | 5|exact_match| 0.0060|± |0.0035| |
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| | |none | 5|bleu_acc | 0.4360|± |0.0222| |
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| | |none | 5|rougeL_max |33.8798|± |0.9367| |
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| | |none | 5|rouge1_max |36.3550|± |0.9462| |
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| | |none | 5|rouge1_acc | 0.3700|± |0.0216| |
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| | |none | 5|acc | 0.2664|± |0.0036| |
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| - mmlu |N/A |none | 0|acc | 0.2533|± |0.0039| |
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| - humanities |N/A |none | 5|acc | 0.2408|± |0.0075| |
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| - other |N/A |none | 5|acc | 0.2443|± |0.0080| |
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| - social_sciences |N/A |none | 5|acc | 0.2538|± |0.0081| |
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| - stem |N/A |none | 5|acc | 0.2740|± |0.0079| |
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| - truthfulqa |N/A |none | 0|rouge2_acc | 0.1920|± |0.0176| |
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| | |none | 0|rougeL_diff|-0.7765|± |1.0034| |
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| | |none | 0|bleu_max |15.2292|± |0.6714| |
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| | |none | 0|rouge2_diff|-3.3178|± |0.9477| |
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| | |none | 0|rougeL_acc | 0.3860|± |0.0218| |
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| | |none | 0|bleu_diff |-0.6500|± |0.6421| |
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| | |none | 0|rouge2_max |16.4873|± |1.0172| |
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| | |none | 0|rouge1_diff|-1.5564|± |1.0223| |
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| | |none | 0|acc | 0.3435|± |0.0137| |
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| | |none | 0|bleu_acc | 0.4360|± |0.0222| |
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| | |none | 0|rougeL_max |33.8798|± |0.9367| |
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| | |none | 0|rouge1_max |36.3550|± |0.9462| |
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| | |none | 0|rouge1_acc | 0.3700|± |0.0216| |
