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Llama3.1-8B-Middo-Alpaca – AI Model by Word2Li | AlphaNeural AI

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Word2Li/
Llama3.1-8B-Middo-Alpaca

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text-generation

Word2Li/MiddOptimized

meta-llama/Llama-3.1-8B

autotrain_compatible

text-generation-inference

endpoints_compatible

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Llama3.1-8B-Middo-Alpaca

Paper: Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning

Code: https://github.com/Word2VecT/Middo

Model description

This model is a fine-tuned version of meta-llama/Llama-3.1-8B on the MiddOptimzed/llama_alpaca dataset.

Training and evaluation data

Training data

Middo optimized tatsu-lab/alpaca on meta-llama/Llama-3.1-8B.

Evaluation data

General
- MMLU
- IFEval
Math
- GSM8K
- MATH
Code
- HumanEval
- MBPP
Reasoning
- Hellaswag
- GPQA

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 4
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 8
total_train_batch_size: 256
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.03
num_epochs: 1.0

Training results

epoch: 0.9988901220865705
total_flos: 8.515440547840655e + 17
train_loss: 1.1814873923195732
train_runtime: 2241.1241
train_samples_per_second: 25.717
train_steps_per_second: 0.1

Framework versions

Transformers 4.45.2
Pytorch 2.5.1+cu121
Datasets 2.21.0
Tokenizers 0.20.1