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---
library_name: transformers
license: llama3.1
base_model: meta-llama/Llama-3.1-8B
language: en
datasets:
- Word2Li/MiddOptimized
tags:
- llama-factory
- full
pipeline_tag: text-generation
model-index:
- name: Llama3.1-8B-Middo-Alpaca-4o-mini
results:
- task:
type: text-generation
dataset:
name: MMLU
type: MMLU
metrics:
- name: weighted accuarcy
type: weighted accuarcy
value: 44.69
verified: true
- task:
type: text-generation
dataset:
name: IFEval
type: IFEval
metrics:
- name: overall accuarcy
type: overall accuarcy
value: 47.96
verified: true
- task:
type: text-generation
dataset:
name: GSM8K
type: GSM8K
metrics:
- name: accuarcy
type: accuarcy
value: 57.62
verified: true
- task:
type: text-generation
dataset:
name: MATH
type: MATH
metrics:
- name: accuarcy
type: accuarcy
value: 18.50
verified: true
- task:
type: text-generation
dataset:
name: HumanEval
type: HumanEval
metrics:
- name: humaneval_pass@1
type: humaneval_pass@1
value: 52.44
verified: true
- task:
type: text-generation
dataset:
name: MBPP
type: MBPP
metrics:
- name: score
type: score
value: 45.40
verified: true
- task:
type: text-generation
dataset:
name: Hellaswag
type: Hellaswag
metrics:
- name: accuarcy
type: accuarcy
value: 57.37
verified: true
- task:
type: text-generation
dataset:
name: GPQA
type: GPQA
metrics:
- name: accuarcy
type: accuarcy
value: 19.70
verified: true
metrics:
- accuracy
---
# Llama3.1-8B-Middo-Alpaca-4o-mini
Paper: [Middo: Model-Informed Dynamic Data Optimization for Enhanced LLM Fine-Tuning via Closed-Loop Learning](https://arxiv.org/abs/2508.21589)
Code: https://github.com/Word2VecT/Middo
## Model description
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) on the [MiddOptimzed/llama_alpaca_4o_mini](https://huggingface.co/datasets/Word2Li/MiddOptimized/viewer/default/llama_alpaca_4o_mini) dataset.
## Training and evaluation data
### Training data
Middo optimized [Word2Li/Alpaca-4o-mini](https://huggingface.co/datasets/Word2Li/Alpaca-4o-mini) on [meta-llama/Llama-3.1-8B](https://huggingface.co/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: 0.9964556962025316
- total_flos: 2.1359726465573192e + 18
- train_loss: 0.9420681825982846
- train_runtime: 3147.8466
- train_samples_per_second: 20.072
- train_steps_per_second: 0.078
### Framework versions
- Transformers 4.45.2
- Pytorch 2.5.1+cu121
- Datasets 2.21.0
- Tokenizers 0.20.1
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