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--- |
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library_name: transformers |
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license: llama3 |
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base_model: meta-llama/Meta-Llama-3-8B-Instruct |
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tags: |
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- axolotl |
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- generated_from_trainer |
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- spectrum finetuning |
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- Deepspeed MultiGPU |
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model-index: |
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- name: Llama-3-8B-spectrum-25 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Llama-3-8B-spectrum-25 |
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This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the [yuvraj17/finetune_alpaca_1K](https://huggingface.co/datasets/yuvraj17/finetune_alpaca_1K) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2791 |
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## Spectrum Fine-tuning: |
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I have used the **Spectrum Fine-tuning** method as described in [Eric Hartford et. al 2024](https://arxiv.org/abs/2406.06623), which selectively targets some ***t%*** of the model layers with the highest **Signal-to-Noise Ratio (SNR)**. By focusing on the most information-dense layers, this approach maximizes fine-tuning efficiency while minimizing compute resources. |
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The key goal of Spectrum Fine-tuning is: *minimize the memory footprint and accelerate LLM training without sacrificing performance.* |
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The 25% layer selection ensures minimal computational overhead for fine-tuning. |
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## Training: |
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- Trained on **2x A40s (48GB VRAM each)** for over 1 hour using the **Axolotl**. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- total_eval_batch_size: 8 |
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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: 100 |
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- num_epochs: 2 |
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### Framework versions |
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- Axolotl 0.4.1 |
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- Transformers 4.44.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
