dataset_1_model

<!-- 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"/>

<details><summary>See axolotl config</summary>

axolotl version: 0.4.0

base_model: croissantllm/CroissantLLMBase                                                                                                                                                                   
model_type: LlamaForCausalLM                                                                                                                                                                                
tokenizer_type: LlamaTokenizerFast                                                                                                                                                                              
is_llama_derived_model: true                                                                                                                                                                                

special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"

tokens:
  - "<|im_start|>"
  - "<|im_end|>"
                                                                                                                                                                                                            
load_in_8bit: false                                                                                                                                                                                         
load_in_4bit: false                                                                                                                                                                                         
strict: false                                                                                                                                                                                               
                                                                                                                                                                                                            
datasets:                                                                                                                                                                                                   
  - path: manu/dataset_1
    split: train                                                                                                                                                              
    type: sharegpt

chat_template: "chatml"
default_system_message: ""

dataset_prepared_path: new_pii  
val_set_size: 0.05                       
output_dir: /gpfs/workdir/fayssema/models/out_translation
                                                                                                                                                                                                            
sequence_len: 2048                                                                                                                                                                                          
sample_packing: false                                                                                                                                                                                       
pad_to_sequence_len: false                                                                                                                                                                                   
                                                                                                                                                                                                            
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: 2
micro_batch_size: 16
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.00003

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
flash_attn_cross_entropy: false
flash_attn_rms_norm: true
flash_attn_fuse_qkv: false
flash_attn_fuse_mlp: true

warmup_steps: 100
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed: #deepspeed_configs/zero2.json # multi-gpu only
weight_decay: 0.05
fsdp:
fsdp_config:

</details><br>

gpfs/workdir/fayssema/models/out_translation

This model is a fine-tuned version of croissantllm/CroissantLLMBase on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0098

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: 3e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 100
• num_epochs: 3

Training results

Framework versions

• Transformers 4.38.0.dev0
• Pytorch 2.3.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0