yuvraj17/Llama-3-8B-spectrum-25-GGUF

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Llama-3-8B-spectrum-25

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the yuvraj17/finetune_alpaca_1K dataset. It achieves the following results on the evaluation set:

• —Loss: 1.2791

Spectrum Fine-tuning:

I have used the Spectrum Fine-tuning method as described in Eric Hartford et. al 2024, 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.

The key goal of Spectrum Fine-tuning is: minimize the memory footprint and accelerate LLM training without sacrificing performance.

The 25% layer selection ensures minimal computational overhead for fine-tuning.

Training:

• —Trained on 2x A40s (48GB VRAM each) for over 1 hour using the Axolotl.

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 0.0002
• —trainbatchsize: 4
• —evalbatchsize: 4
• —seed: 42
• —distributed_type: multi-GPU
• —num_devices: 2
• —gradientaccumulationsteps: 4
• —totaltrainbatch_size: 32
• —totalevalbatch_size: 8
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: cosine
• —lrschedulerwarmup_steps: 100
• —num_epochs: 2

Framework versions

• —Axolotl 0.4.1
• —Transformers 4.44.2
• —Pytorch 2.4.0+cu121
• —Datasets 2.20.0
• —Tokenizers 0.19.1