---
license: mit
base_model: croissantllm/CroissantLLMBase
tags:
- generated_from_trainer
model-index:
- name: out
  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: croissantllm/CroissantLLMBase                                                                                                                                                                   
model_type: LlamaForCausalLM                                                                                                                                                                                
tokenizer_type: LlamaTokenizerFast                                                                                                                                                                              
is_llama_derived_model: true                                                                                                                                                                                
                                                                                                                                                                                                            
load_in_8bit: false                                                                                                                                                                                         
load_in_4bit: false                                                                                                                                                                                         
strict: false                                                                                                                                                                                               
                                                                                                                                                                                                            
datasets:                                                                                                                                                                                                   
  - path: manu/mmlu_auxiliary_train_formatted_2
    split: train                                                                                                                                                               
    type: completion                                                                                                                                                                                        

dataset_prepared_path: last_run_prepared                                                                                                                                                                    
val_set_size: 0.05                                                                                                                                                                                          
output_dir: ./out                                                                                                                                                                                           
                                                                                                                                                                                                            
sequence_len: 2048                                                                                                                                                                                          
sample_packing: true                                                                                                                                                                                       
pad_to_sequence_len: true                                                                                                                                                                                   
                                                                                                                                                                                                            
adapter:                                                                                                                                                                                                    
lora_model_dir:                                                                                                                                                                                             
lora_r:                                                                                                                                                                                                     
lora_alpha:                                                                                                                                                                                                 
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 16
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

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

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
flash_attn_cross_entropy: false
flash_attn_rms_norm: true
flash_attn_fuse_qkv: false
flash_attn_fuse_mlp: true

warmup_steps: 50
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed: #deepspeed_configs/zero2.json # multi-gpu only
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:

```

</details><br>

# out

This model is a fine-tuned version of [croissantllm/CroissantLLMBase](https://huggingface.co/croissantllm/CroissantLLMBase) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7378

## 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: 50
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5429        | 0.0   | 1    | 2.5242          |
| 2.2283        | 0.25  | 69   | 2.2514          |
| 1.9539        | 0.5   | 138  | 2.0381          |
| 1.6608        | 0.75  | 207  | 1.6872          |
| 1.3767        | 1.0   | 276  | 1.3323          |
| 0.7872        | 1.23  | 345  | 1.0583          |
| 0.5873        | 1.48  | 414  | 0.8251          |
| 0.5154        | 1.73  | 483  | 0.7378          |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0