--- library_name: transformers tags: - function calling - laser license: apache-2.0 datasets: - jtatman/glaive_function_calling_v2_filtered_10k --- # Model Card 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). ### Model Description 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. ## Uses 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. ### Direct Use - Chat - Conversational - Text Generation - Function Calling ## Bias, Risks, and Limitations 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. ### Recommendations Use at your own risk. It's a great small model, owing to the base model before tuning. ## Training Details ### Training Data - "eval/loss": 2.1797242164611816, - "_timestamp": 1708624900.2239263, - "_runtime": 20945.370138406754, - "train/train_loss": 2.515587423102269, - "train/global_step": 918, - "train/train_steps_per_second": 0.044, - "train/loss": 2.2062, - "train/learning_rate": 0, - "train/train_samples_per_second": 1.403, - "train/train_runtime": 20945.6359, - "eval/steps_per_second": 4.867, - "eval/samples_per_second": 4.867, - "_step": 923, - "train/epoch": 2.98, - "eval/runtime": 41.0972, - "train/grad_norm": 0.2638521194458008, - "train/total_flos": 141790931224363000 ### Training Procedure [LaserRMT](https://github.com/cognitivecomputations/laserRMT) was used to refine the weights, using the 16 highest scored weights specifically by noise-to-ratio analysis. This technique avoids training unnecessarily low-performng weights that can turn to garbage. By pruning these weights, the model size is decreased slightly.  Axolotl was used for training and dataset tokenization. #### Preprocessing Dataset was formatted in ShareGpt format for the purposes of using with Axolotl, in conversational format. #### Training Hyperparameters - lora_r: 64 - lora_alpha: 16 - lora_dropout: 0.05 - gradient_accumulation_steps: 4 - micro_batch_size: 1 - num_epochs: 3 - optimizer: adamw_bnb_8bit - lr_scheduler: cosine - learning_rate: 0.00025 #### Evaluation | Groups |Version| Filter |n-shot| Metric | Value | |Stderr| |--------------------|-------|----------------|-----:|-----------|------:|---|-----:| |Open LLM Leaderboard|N/A |none | 5|rouge2_acc | 0.1920|± |0.0176| | | |none | 5|bleu_max |15.2292|± |0.6714| | | |flexible-extract| 5|exact_match| 0.0220|± |0.0066| | - truthfulqa_mc1 | 2|none | 0|acc | 0.2440|± |0.0192| | - truthfulqa_mc2 | 2|none | 0|acc | 0.4430|± |0.0195| | - winogrande | 1|none | 5|acc | 0.5120|± |0.0224| | - arc_challenge | 1|none | 25|acc | 0.1760|± |0.0170| | | |none | 25|acc_norm | 0.2320|± |0.0189| | - gsm8k | 3|strict-match | 5|exact_match| 0.0060|± |0.0035| | | |flexible-extract| 5|exact_match| 0.0220|± |0.0066| | - hellaswag | 1|none | 10|acc | 0.3520|± |0.0214| | | |none | 10|acc_norm | 0.4040|± |0.0220| | | |none | 5|rouge2_diff|-3.3178|± |0.9477| | | |none | 5|rougeL_acc | 0.3860|± |0.0218| | | |none | 5|acc_norm | 0.3180|± |0.0145| | | |none | 5|rouge1_diff|-1.5564|± |1.0223| | | |none | 5|bleu_diff |-0.6500|± |0.6421| | | |none | 5|rouge2_max |16.4873|± |1.0172| | | |none | 5|rougeL_diff|-0.7765|± |1.0034| | | |strict-match | 5|exact_match| 0.0060|± |0.0035| | | |none | 5|bleu_acc | 0.4360|± |0.0222| | | |none | 5|rougeL_max |33.8798|± |0.9367| | | |none | 5|rouge1_max |36.3550|± |0.9462| | | |none | 5|rouge1_acc | 0.3700|± |0.0216| | | |none | 5|acc | 0.2664|± |0.0036| | - mmlu |N/A |none | 0|acc | 0.2533|± |0.0039| | - humanities |N/A |none | 5|acc | 0.2408|± |0.0075| | - other |N/A |none | 5|acc | 0.2443|± |0.0080| | - social_sciences |N/A |none | 5|acc | 0.2538|± |0.0081| | - stem |N/A |none | 5|acc | 0.2740|± |0.0079| | - truthfulqa |N/A |none | 0|rouge2_acc | 0.1920|± |0.0176| | | |none | 0|rougeL_diff|-0.7765|± |1.0034| | | |none | 0|bleu_max |15.2292|± |0.6714| | | |none | 0|rouge2_diff|-3.3178|± |0.9477| | | |none | 0|rougeL_acc | 0.3860|± |0.0218| | | |none | 0|bleu_diff |-0.6500|± |0.6421| | | |none | 0|rouge2_max |16.4873|± |1.0172| | | |none | 0|rouge1_diff|-1.5564|± |1.0223| | | |none | 0|acc | 0.3435|± |0.0137| | | |none | 0|bleu_acc | 0.4360|± |0.0222| | | |none | 0|rougeL_max |33.8798|± |0.9367| | | |none | 0|rouge1_max |36.3550|± |0.9462| | | |none | 0|rouge1_acc | 0.3700|± |0.0216|