README.md

---
base_model:
- elinas/Llama-3-15B-Instruct-zeroed
library_name: transformers
tags:
- mergekit
- merge
- finetune
datasets:
- Chat-Error/Pure-dove-sharegpt
license: llama3
---
# Llama-3-15B-Instruct-zeroed-ft-v2

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 of all LoRA modules.

The model was finetuned on **8192 context length** and it can possibly be extended using RoPE up to 32k.

**v3 of the model will contain significantly more data, primarily human focused, aimed to excel at writing as well as maintaining logic, coherency, and continuity.**

**[GGUF Quants provided by @gelukuMLG](https://huggingface.co/gelukuMLG/Llama-3-15B-Instruct-ft-v2-GGUF)**

## Datasets

* [Chat-Error/Pure-dove-sharegpt](https://huggingface.co/datasets/Chat-Error/Pure-dove-sharegpt)

A small, high quality, curated dataset was used as a PoC / validation on stabilizing the model after the original passthrough merge. 

## Finetuning details
This is a QLoRA model and all of the LoRA modules were targeted this time to ensure sufficient training before moving on to larger datasets. 
the first version of this model only targeted **o_proj** and **up_proj**
```yaml
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj
lora_modules_to_save:
  - embed_tokens
  - lm_head
```

The model is coherent even with training the "zeroed" layers plus the additional layers, as this was the recommendation from [Charles Goddard](https://huggingface.co/chargoddard) (mergekit developer) - 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- total_train_batch_size: 3
- total_eval_batch_size: 3
- 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 4 hours total.

**Unsloth** was used for speed and memory savings.

Sample packing and padding was disabled to reduce VRAM consumption significantly at the cost of speed.

W&B Run Summary
```
wandb:                eval/loss 0.90895
wandb:             eval/runtime 463.4688
wandb:  eval/samples_per_second 0.833
wandb:    eval/steps_per_second 0.278
wandb:               total_flos 8270790524928.0
wandb:              train/epoch 1.0
wandb:        train/global_step 1157
wandb:          train/grad_norm 7.3847
wandb:      train/learning_rate 0.0
wandb:               train/loss 0.8702
wandb:               train_loss 0.87814
wandb:            train_runtime 16425.2713
wandb: train_samples_per_second 0.211
wandb:   train_steps_per_second 0.07
```

### Framework versions

- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1

## 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)