manu/dataset_1_model

See axolotl config

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:

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

Training Loss	Epoch	Step	Validation Loss
2.6652	0.0	1	2.0261
0.2986	0.25	73	0.0199
0.19	0.5	146	0.0136
0.3032	0.76	219	0.0158
0.1343	1.01	292	0.0125
0.12	1.26	365	0.0117
0.2266	1.51	438	0.0113
0.1924	1.77	511	0.0097
0.1448	2.02	584	0.0095
0.0718	2.27	657	0.0098
0.1184	2.52	730	0.0097
0.1124	2.77	803	0.0098

Framework versions

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