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metadata
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.

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 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

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