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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-humaneval
model-index:
- name: CodeLlama-34b-Instruct-sft-5e-3-epoch-100-human-eval-final
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/93nixyw1)
# CodeLlama-34b-Instruct-sft-5e-3-epoch-100-human-eval-final
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-humaneval dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7616
- Loss Layer 6 Head: 1.0709
- Loss Layer 12 Head: 0.8047
- Loss Layer 18 Head: 0.7212
- Loss Layer 24 Head: 0.4396
- Loss Layer 30 Head: 0.3042
- Loss Layer 36 Head: 0.2040
- Loss Layer 42 Head: 0.1346
## 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 |
|:-------------:|:-------:|:----:|:---------------:|:-----------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|
| 3.7028 | 9.1168 | 200 | 4.6352 | 1.4035 | 0.8311 | 1.0429 | 0.4931 | 0.4457 | 0.2349 | 0.1599 |
| 2.736 | 18.2336 | 400 | 4.7219 | 1.2158 | 0.8316 | 0.7490 | 1.0869 | 0.3238 | 0.2666 | 0.1723 |
| 2.0128 | 27.3504 | 600 | 3.8953 | 1.1598 | 0.8030 | 0.7230 | 0.4451 | 0.3500 | 0.2027 | 0.1459 |
| 3.3605 | 36.4672 | 800 | 4.9203 | 1.1038 | 0.8175 | 1.6655 | 0.4410 | 0.3091 | 0.2055 | 0.1365 |
| 2.5177 | 45.5840 | 1000 | 4.2388 | 1.0907 | 0.8042 | 1.1115 | 0.4403 | 0.3038 | 0.2217 | 0.1412 |
| 2.0743 | 54.7009 | 1200 | 3.9221 | 1.0727 | 0.8050 | 0.8689 | 0.4418 | 0.3012 | 0.2044 | 0.1362 |
| 1.8844 | 63.8177 | 1400 | 3.8140 | 1.0723 | 0.8028 | 0.7729 | 0.4389 | 0.3045 | 0.2036 | 0.1350 |
| 1.8019 | 72.9345 | 1600 | 3.7777 | 1.0726 | 0.8038 | 0.7376 | 0.4401 | 0.3042 | 0.2032 | 0.1345 |
| 1.7339 | 82.0513 | 1800 | 3.7662 | 1.0703 | 0.8056 | 0.7246 | 0.4394 | 0.3041 | 0.2042 | 0.1347 |
| 1.6981 | 91.1681 | 2000 | 3.7616 | 1.0709 | 0.8047 | 0.7212 | 0.4396 | 0.3042 | 0.2040 | 0.1346 |
### Framework versions
- Transformers 4.43.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.19.1
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