---
license: llama2
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: codellama/CodeLlama-7b-hf
model-index:
- name: camel-lora
  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: `0.4.0`
```yaml
base_model: codellama/CodeLlama-7b-hf
model_type: LlamaForCausalLM
tokenizer_type: CodeLlamaTokenizer
is_llama_derived_model: true

hub_model_id: noeloco/camel-lora

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: noeloco/fizzbuzz-sft
    type: alpaca
    ds_type: json

hf_use_auth_token: true
push_dataset_to_hub: noeloco
val_set_size: 0.05
output_dir: ./lora-out
chat_template: chatml

sequence_len: 4096
sample_packing: false
pad_to_sequence_len: true

adapter: qlora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: runpod1
wandb_entity:
wandb_watch:
wandb_name: 
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: auto
fp16: false
tf32: true

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
debug: true
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"

```

</details><br>

# camel-lora

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: 0.0290

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7685        | 0.06  | 1    | 2.5524          |
| 1.8762        | 0.29  | 5    | 2.4927          |
| 1.215         | 0.57  | 10   | 1.4546          |
| 0.484         | 0.86  | 15   | 0.7250          |
| 0.3667        | 1.14  | 20   | 0.4146          |
| 0.1638        | 1.43  | 25   | 0.2123          |
| 0.2948        | 1.71  | 30   | 0.0980          |
| 0.2003        | 2.0   | 35   | 0.0629          |
| 0.0888        | 2.29  | 40   | 0.0577          |
| 0.0918        | 2.57  | 45   | 0.0414          |
| 0.0931        | 2.86  | 50   | 0.0363          |
| 0.0982        | 3.14  | 55   | 0.0304          |
| 0.0849        | 3.43  | 60   | 0.0289          |
| 0.0511        | 3.71  | 65   | 0.0290          |


### Framework versions

- PEFT 0.10.1.dev0
- Transformers 4.40.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0