---
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](https://github.com/OpenAccess-AI-Collective/axolotl/blob/main/image/axolotl-badge-web.png?raw=true)

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|