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---
license: llama2
base_model: meta-llama/CodeLlama-13b-Instruct-hf
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- meng-lab/CodeLlama-13B-Instruct-gsm8k
model-index:
- name: CodeLlama-13b-Instruct-sft-5e-3-epoch-100-gsm8k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/uva-llm/huggingface/runs/26r5nw5b)
# CodeLlama-13b-Instruct-sft-5e-3-epoch-100-gsm8k
This model is a fine-tuned version of [meta-llama/CodeLlama-13b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-13b-Instruct-hf) on the meng-lab/CodeLlama-13B-Instruct-gsm8k dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0229
- Loss Layer 5 Head: 1.4382
- Loss Layer 10 Head: 0.9813
- Loss Layer 15 Head: 0.9315
- Loss Layer 20 Head: 0.4901
- Loss Layer 25 Head: 0.1839
- Loss Layer 30 Head: 0.1044
- Loss Layer 35 Head: 0.1004
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.005
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Loss Layer 5 Head | Loss Layer 10 Head | Loss Layer 15 Head | Loss Layer 20 Head | Loss Layer 25 Head | Loss Layer 30 Head | Loss Layer 35 Head |
|:-------------:|:-------:|:----:|:---------------:|:-----------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|
| 3.5888 | 26.0163 | 200 | 4.9539 | 1.5721 | 1.0672 | 1.1373 | 0.7569 | 0.2971 | 0.1321 | 0.2111 |
| 2.2226 | 52.0325 | 400 | 4.1476 | 1.4725 | 0.9947 | 0.9848 | 0.4952 | 0.1877 | 0.1073 | 0.1141 |
| 1.9091 | 78.0488 | 600 | 4.0229 | 1.4382 | 0.9813 | 0.9315 | 0.4901 | 0.1839 | 0.1044 | 0.1004 |
### Framework versions
- Transformers 4.43.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.19.1
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