Quantization made by Richard Erkhov.

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opt-history-v2 - AWQ
- Model creator: https://huggingface.co/ambrosfitz/
- Original model: https://huggingface.co/ambrosfitz/opt-history-v2/




Original model description:
---
license: other
base_model: facebook/opt-350m
tags:
- generated_from_trainer
model-index:
- name: opt-history-gen2
  results: []
datasets:
- ambrosfitz/just_history
- ambrosfitz/synth_history_sentences
- ambrosfitz/ps_history_txt
- ambrosfitz/ah_analysis_multihistory
language:
- en
pipeline_tag: text-generation
---

<!-- 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. -->

# opt-history-gen2

This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8025

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.1245        | 0.1693 | 100  | 3.0013          |
| 2.9473        | 0.3386 | 200  | 2.9180          |
| 2.9014        | 0.5078 | 300  | 2.8787          |
| 2.873         | 0.6771 | 400  | 2.8646          |
| 2.8631        | 0.8464 | 500  | 2.8631          |
| 2.8366        | 1.0157 | 600  | 2.8451          |
| 2.64          | 1.1849 | 700  | 2.8380          |
| 2.6295        | 1.3542 | 800  | 2.8202          |
| 2.6272        | 1.5235 | 900  | 2.7987          |
| 2.6327        | 1.6928 | 1000 | 2.7971          |
| 2.626         | 1.8620 | 1100 | 2.7739          |
| 2.5237        | 2.0313 | 1200 | 2.7829          |
| 2.3242        | 2.2006 | 1300 | 2.7812          |
| 2.319         | 2.3699 | 1400 | 2.7727          |
| 2.3314        | 2.5391 | 1500 | 2.7668          |
| 2.3579        | 2.7084 | 1600 | 2.7561          |
| 2.307         | 2.8777 | 1700 | 2.7586          |
| 2.2612        | 3.0470 | 1800 | 2.7795          |
| 2.056         | 3.2163 | 1900 | 2.7801          |
| 2.0802        | 3.3855 | 2000 | 2.7670          |
| 2.1104        | 3.5548 | 2100 | 2.7708          |
| 2.1115        | 3.7241 | 2200 | 2.7629          |
| 2.0828        | 3.8934 | 2300 | 2.7606          |
| 1.996         | 4.0626 | 2400 | 2.7849          |
| 1.8701        | 4.2319 | 2500 | 2.7938          |
| 1.92          | 4.4012 | 2600 | 2.7928          |
| 1.8844        | 4.5705 | 2700 | 2.7846          |
| 1.9058        | 4.7397 | 2800 | 2.7840          |
| 1.901         | 4.9090 | 2900 | 2.7821          |
| 1.8418        | 5.0783 | 3000 | 2.8017          |
| 1.757         | 5.2476 | 3100 | 2.8055          |
| 1.7503        | 5.4168 | 3200 | 2.8095          |
| 1.7606        | 5.5861 | 3300 | 2.8065          |
| 1.7316        | 5.7554 | 3400 | 2.8043          |
| 1.7632        | 5.9247 | 3500 | 2.8025          |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1