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---
license: llama2
base_model: meta-llama/CodeLlama-34b-Instruct-hf
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- meng-lab/CodeLlama-34B-Instruct-gsm8k
model-index:
- name: CodeLlama-34b-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/nemwhwry)
# CodeLlama-34b-Instruct-sft-5e-3-epoch-100-gsm8k
This model is a fine-tuned version of [meta-llama/CodeLlama-34b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-34b-Instruct-hf) on the meng-lab/CodeLlama-34B-Instruct-gsm8k dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0230
- Loss Layer 6 Head: 1.2898
- Loss Layer 12 Head: 1.0049
- Loss Layer 18 Head: 0.9093
- Loss Layer 24 Head: 0.4408
- Loss Layer 30 Head: 0.2683
- Loss Layer 36 Head: 0.1391
- Loss Layer 42 Head: 0.0639
## 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 6 Head | Loss Layer 12 Head | Loss Layer 18 Head | Loss Layer 24 Head | Loss Layer 30 Head | Loss Layer 36 Head | Loss Layer 42 Head |
|:-------------:|:-------:|:----:|:---------------:|:-----------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|
| 2.6241 | 25.8065 | 200 | 4.3768 | 1.3707 | 1.0927 | 0.9492 | 0.4907 | 0.2888 | 0.1534 | 0.0899 |
| 1.6189 | 51.6129 | 400 | 4.0476 | 1.3067 | 0.9916 | 0.9104 | 0.4445 | 0.2716 | 0.1405 | 0.0663 |
| 1.3737 | 77.4194 | 600 | 4.0230 | 1.2898 | 1.0049 | 0.9093 | 0.4408 | 0.2683 | 0.1391 | 0.0639 |
### Framework versions
- Transformers 4.43.2
- Pytorch 2.1.2
- Datasets 3.2.0
- Tokenizers 0.19.1
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