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---
base_model:
- elinas/Llama-3-15B-Instruct-zeroed
library_name: transformers
tags:
- mergekit
- merge
datasets:
- Chat-Error/Pure-dove-sharegpt
license: llama3
---
# Llama-3-15B-Instruct-zeroed-ft
This is a QLoRA **finetune** of a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
The model is based on a "zeroed" passthrough merge of [Llama-3-15B-Instruct-zeroed](https://huggingface.co/elinas/Llama-3-15B-Instruct-zeroed)
This was primarily an experiment to see how a passthrough merge will respond to further finetuning, though this was done on a small dataset.
The model was finetuned on **8192 context length** and is likely reliable using RoPE up to 32k.
Further finetuning this model or finetuning the [base model](https://huggingface.co/elinas/Llama-3-15B-Instruct-zeroed) on more samples is encouraged.
## Datasets
* [Chat-Error/Pure-dove-sharegpt](https://huggingface.co/datasets/Chat-Error/Pure-dove-sharegpt)
A small, high quality, dataset was used as a PoC / validation on stabilizing the model after finetuning.
## Finetuning details
This is a QLoRA model and the following modules were targeted.
```yaml
lora_target_modules:
- down_proj
- o_proj
```
The model is coherent even with training the "zeroed" layers and can write well. In the next experiment, all layers will be finetuned as this was
the recommendation from [Charles Goddard](https://huggingface.co/chargoddard) - thank you for sharing the method of merging as well as Toasty
Pigeon for bringing it to my attention!
```yaml
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- total_train_batch_size: 6
- total_eval_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 25
- num_epochs: 1
```
Optimizer `paged_adamw_8bit` and Deepspeed ZeRO 3 was used at a LR of `1e-5` using the cosine scheduler for 1 epoch on 3x3090s taking 2h 30m total.
Sample packing and padding was disabled to reduce VRAM consumption significantly at the cost of speed.
W&B Run Summary
```
wandb: Run summary:
wandb: eval/loss 0.94497
wandb: eval/runtime 276.2864
wandb: eval/samples_per_second 1.397
wandb: eval/steps_per_second 0.235
wandb: total_flos 12246605365248.0
wandb: train/epoch 1.0
wandb: train/global_step 579
wandb: train/grad_norm 0.80411
wandb: train/learning_rate 0.0
wandb: train/loss 1.085
wandb: train_loss 0.8834
wandb: train_runtime 9893.1688
wandb: train_samples_per_second 0.351
wandb: train_steps_per_second 0.059
```
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
## Model Evaluation
TBD
If you have any questions or comments on the model, feel free to open a discussion in the community tab.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |