---
license: llama2
base_model: codellama/CodeLlama-7b-hf
tags:
- generated_from_trainer
model-index:
- name: karim_codellama
  results: []
library_name: peft
---

<!-- 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/llm_project/llm_project-org/runs/nb0hywqq)
# karim_codellama

This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1887

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- _load_in_8bit: True
- _load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
- bnb_4bit_quant_storage: uint8
- load_in_4bit: False
- load_in_8bit: True
### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 400
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.146         | 0.0787 | 20   | 1.2504          |
| 0.8176        | 0.1573 | 40   | 0.6454          |
| 0.6291        | 0.2360 | 60   | 0.4881          |
| 0.3068        | 0.3147 | 80   | 0.3589          |
| 0.5266        | 0.3933 | 100  | 0.4066          |
| 0.302         | 0.4720 | 120  | 0.2728          |
| 0.1989        | 0.5506 | 140  | 0.2604          |
| 0.3157        | 0.6293 | 160  | 0.2502          |
| 0.1768        | 0.7080 | 180  | 0.2285          |
| 0.4553        | 0.7866 | 200  | 0.2575          |
| 0.2183        | 0.8653 | 220  | 0.2152          |
| 0.1815        | 0.9440 | 240  | 0.2148          |
| 0.2704        | 1.0226 | 260  | 0.2142          |
| 0.1662        | 1.1013 | 280  | 0.2001          |
| 0.3306        | 1.1799 | 300  | 0.2065          |
| 0.2161        | 1.2586 | 320  | 0.1967          |
| 0.1429        | 1.3373 | 340  | 0.1925          |
| 0.2892        | 1.4159 | 360  | 0.1927          |
| 0.1459        | 1.4946 | 380  | 0.1894          |
| 0.3078        | 1.5733 | 400  | 0.1887          |


### Framework versions

- PEFT 0.6.0.dev0
- Transformers 4.41.0.dev0
- Pytorch 2.1.2
- Datasets 2.19.0
- Tokenizers 0.19.1