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---
license: llama2
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: codellama/CodeLlama-7b-hf
model-index:
- name: EvilCodeLlama-7b
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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.3.0`
```yaml
base_model: codellama/CodeLlama-7b-hf
base_model_config: codellama/CodeLlama-7b-hf
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true
hub_model_id: EvilCodeLlama-7b
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: dhuynh95/Magicoder-Evol-Instruct-110K-Filtered_0.35
type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.02
output_dir: ./qlora-out-evil-codellama
adapter: qlora
lora_model_dir:
eval_sample_packing: false
sequence_len: 2048
sample_packing: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project: axolotl
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 16
num_epochs: 1
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: true
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
eval_steps: 0.01
save_strategy: epoch
save_steps:
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
bos_token: "<s>"
eos_token: "</s>"
unk_token: "<unk>"
```
</details><br>
# EvilCodeLlama-7b
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: 1.1701
## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2543 | 0.04 | 1 | 1.2447 |
| 1.2781 | 0.08 | 2 | 1.2445 |
| 1.2677 | 0.12 | 3 | 1.2446 |
| 1.2725 | 0.16 | 4 | 1.2447 |
| 1.2704 | 0.21 | 5 | 1.2440 |
| 1.2572 | 0.25 | 6 | 1.2442 |
| 1.2875 | 0.29 | 7 | 1.2439 |
| 1.2672 | 0.33 | 8 | 1.2434 |
| 1.2601 | 0.37 | 9 | 1.2430 |
| 1.2808 | 0.41 | 10 | 1.2421 |
| 1.2665 | 0.45 | 11 | 1.2411 |
| 1.2572 | 0.49 | 12 | 1.2400 |
| 1.2505 | 0.54 | 13 | 1.2384 |
| 1.264 | 0.58 | 14 | 1.2365 |
| 1.2809 | 0.62 | 15 | 1.2338 |
| 1.2054 | 0.66 | 16 | 1.2308 |
| 1.2732 | 0.7 | 17 | 1.2269 |
| 1.2586 | 0.74 | 18 | 1.2219 |
| 1.2939 | 0.78 | 19 | 1.2161 |
| 1.2713 | 0.82 | 20 | 1.2086 |
| 1.2154 | 0.87 | 21 | 1.2008 |
| 1.213 | 0.91 | 22 | 1.1917 |
| 1.2183 | 0.95 | 23 | 1.1813 |
| 1.1594 | 0.99 | 24 | 1.1701 |
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.37.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.0 |