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