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---
library_name: transformers
license: apache-2.0
datasets:
- Open-Orca/SlimOrca
pipeline_tag: text-generation
base_model: Na0s/Llama-3.1-8b-Pruned-4-Layers
---
<a href="https://ibb.co/0Yhg31Q"><img src="https://i.ibb.co/F8gStcn/Model-card-peft-lora.webp" alt="Model-card-peft-lora" align="center"></a>
# Model Card for Na0s/Llama-3.1-8B-Pruned-4-Layers_LoRA-PEFT
## Model Details
### Model Description
- **Finetuned from model:[Na0s/Llama-3.1-8b-Pruned-4-Layers]**
## Training Details
LoRA BF16,
batch_size=2,
steps=10000, gradient_accumulation_steps = 4,
warmup_steps = 5,
max_steps = 10000
learning_rate = 2e-4,
fp16 = not is_bfloat16_supported(),
bf16 = is_bfloat16_supported(),
logging_steps = 1,
optim = "adamw_8bit",
weight_decay = 0.01,
lr_scheduler_type = "linear",
seed = 3407
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[Open-Orca/SlimOrca]
## Evaluation
MMLU Pro 0-shot: 0.2937
#### Evaluation Data
<!-- This should link to a Dataset Card if possible. -->
[TIGER-AI-Lab/MMLU-Pro]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |