See axolotl config

axolotl version: 0.4.0

base_model: argilla/notus-7b-v1
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true

hub_model_id: AlekseyKorshuk/notus-casino-reviews
hub_strategy: every_save

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: AlekseyKorshuk/casino-reviews
    type: sharegpt
    conversation: zephyr

dataset_prepared_path:
val_set_size: 0.002
output_dir: ./output

sequence_len: 2048
sample_packing: false
pad_to_sequence_len:

lora_r:
lora_alpha:
lora_dropout:
lora_target_modules:
lora_target_linear:
lora_fan_in_fan_out:

wandb_project: casino-reviews
wandb_entity:
wandb_watch:
wandb_name: notus-7b-v1
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 16
num_epochs: 1
optimizer: paged_adamw_8bit
adam_beta1: 0.9
adam_beta2: 0.95
max_grad_norm: 1.0
adam_epsilon: 0.00001
lr_scheduler: cosine
cosine_min_lr_ratio: 0.1
learning_rate: 2e-5
warmup_ratio: 0.1
weight_decay: 0.01

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: true
#float16: false
#bloat16: true

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true


evals_per_epoch: 2
eval_table_size: 8 # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
eval_table_max_new_tokens: 768 # Total number of tokens generated for predictions sent to wandb. Default is 128
eval_sample_packing: false

saves_per_epoch: 1
save_total_limit: 1
seed: 42
debug:
deepspeed:

fsdp:
fsdp_config:
resize_token_embeddings_to_32x: true

notus-casino-reviews

This model is a fine-tuned version of argilla/notus-7b-v1 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 1.1794

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• total_train_batch_size: 128
• total_eval_batch_size: 128
• optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 5
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
3.2587	0.0	1	3.2501
1.1679	0.5	214	1.1794

Framework versions

• Transformers 4.38.0.dev0
• Pytorch 2.0.1+cu118
• Datasets 2.16.1
• Tokenizers 0.15.0