---
library_name: transformers
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- HuggingFaceTB/Magpie-Pro-300K-Filtered-H4
- HuggingFaceTB/self-oss-instruct-sc2-H4
- HuggingFaceTB/OpenHermes-2.5-H4
- HuggingFaceTB/everyday-conversations-llama3.1-2k
- HuggingFaceTB/instruct-data-basics-smollm-H4
model-index:
- name: monet-vd-1.4B-100BT-chat-hf
  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. -->

# monet-vd-1.4B-100BT-chat-hf

This model is a fine-tuned version of [monet-vd-1.4B-100BT-hf](https://huggingface.co/MonetLLM/monet-vd-1.4B-100BT-hf) on the HuggingFaceTB/Magpie-Pro-300K-Filtered-H4, the HuggingFaceTB/self-oss-instruct-sc2-H4, the HuggingFaceTB/OpenHermes-2.5-H4, the HuggingFaceTB/everyday-conversations-llama3.1-2k and the HuggingFaceTB/instruct-data-basics-smollm-H4 datasets.
It achieves the following results on the evaluation set:
- Loss: 1.1664

## 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: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.8032        | 0.9988 | 502  | 1.1664          |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1
- Datasets 2.21.0
- Tokenizers 0.19.1