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---
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
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