manu / CroissantLLM-PII-filter

huggingface.co
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Model's Last Updated: February 22 2024
text-generation

Introduction of CroissantLLM-PII-filter

Model Details of CroissantLLM-PII-filter

Built with Axolotl

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/pii-masking
    split: train                                                                                                                                                              
    type: sharegpt

chat_template: "chatml"
default_system_message: "Rewrite the sentence to remove the PII."

dataset_prepared_path: last_pii  
val_set_size: 0.05                       
output_dir: ./out_pii
                                                                                                                                                                                                            
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: 1
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:

out_pii

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

  • Loss: 0.0082
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: 1
Training results
Training Loss Epoch Step Validation Loss
6.0535 0.0 1 6.2368
0.0107 0.25 783 0.0137
0.013 0.5 1566 0.0098
0.0077 0.75 2349 0.0082
Framework versions
  • Transformers 4.37.2
  • Pytorch 2.1.2+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0

Runs of manu CroissantLLM-PII-filter on huggingface.co

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More Information About CroissantLLM-PII-filter huggingface.co Model

More CroissantLLM-PII-filter license Visit here:

https://choosealicense.com/licenses/mit

CroissantLLM-PII-filter huggingface.co

CroissantLLM-PII-filter huggingface.co is an AI model on huggingface.co that provides CroissantLLM-PII-filter's model effect (), which can be used instantly with this manu CroissantLLM-PII-filter model. huggingface.co supports a free trial of the CroissantLLM-PII-filter model, and also provides paid use of the CroissantLLM-PII-filter. Support call CroissantLLM-PII-filter model through api, including Node.js, Python, http.

CroissantLLM-PII-filter huggingface.co Url

https://huggingface.co/manu/CroissantLLM-PII-filter

manu CroissantLLM-PII-filter online free

CroissantLLM-PII-filter huggingface.co is an online trial and call api platform, which integrates CroissantLLM-PII-filter's modeling effects, including api services, and provides a free online trial of CroissantLLM-PII-filter, you can try CroissantLLM-PII-filter online for free by clicking the link below.

manu CroissantLLM-PII-filter online free url in huggingface.co:

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CroissantLLM-PII-filter install

CroissantLLM-PII-filter is an open source model from GitHub that offers a free installation service, and any user can find CroissantLLM-PII-filter on GitHub to install. At the same time, huggingface.co provides the effect of CroissantLLM-PII-filter install, users can directly use CroissantLLM-PII-filter installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

CroissantLLM-PII-filter install url in huggingface.co:

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