---
base_model: microsoft/Phi-3.5-mini-instruct
datasets:
- generator
library_name: peft
license: mit
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Phi-3.5-MultiCap-tool-embedding-past
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Phi-3.5-MultiCap-tool-embedding-past

This model is a fine-tuned version of [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7561

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.1082        | 0.1524 | 50   | 1.1135          |
| 0.9647        | 0.3048 | 100  | 1.0051          |
| 0.9516        | 0.4571 | 150  | 0.9498          |
| 0.8882        | 0.6095 | 200  | 0.9027          |
| 0.9183        | 0.7619 | 250  | 0.8649          |
| 0.7923        | 0.9143 | 300  | 0.8355          |
| 0.8078        | 1.0667 | 350  | 0.8137          |
| 0.7677        | 1.2190 | 400  | 0.7969          |
| 0.765         | 1.3714 | 450  | 0.7822          |
| 0.812         | 1.5238 | 500  | 0.7720          |
| 0.7376        | 1.6762 | 550  | 0.7638          |
| 0.7617        | 1.8286 | 600  | 0.7586          |
| 0.7299        | 1.9810 | 650  | 0.7561          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1