YoungPanda/Legal_Kola

See axolotl config

axolotl version: 0.4.0

base_model: mistralai/Mistral-7B-Instruct-v0.2
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: KolaGang/privacy_sumsum
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false


output_dir: ./out
lisa_n_layers: 4
lisa_step_interval: 20
lisa_layers_attribute: model.layers

wandb_project: mistral_law
wandb_entity:
wandb_watch:
wandb_name: mistral_law
wandb_log_model: 

gradient_accumulation_steps: 2
micro_batch_size: 10
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000005

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: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

out

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.9301

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: 5e-06
• train_batch_size: 10
• eval_batch_size: 10
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 2
• total_train_batch_size: 160
• total_eval_batch_size: 80
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 10
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
4.4237	0.02	1	2.8640
0.9506	0.26	13	1.5696
0.5752	0.53	26	1.0073
0.5111	0.79	39	0.9301

Framework versions

• Transformers 4.40.0.dev0
• Pytorch 2.1.1
• Datasets 2.18.0
• Tokenizers 0.15.0