Hugging Face's logo

strickvl
/
isafpr-tiny-llama-lora-sharegpt

PEFT
Safetensors
llama
axolotl
Generated from Trainer
4-bit precision
bitsandbytes
Model card Files Community
isafpr-tiny-llama-lora-sharegpt / README.md
strickvl's picture
strickvl
End of training
82b2d03 verified
preview code
|
raw
history blame contribute delete
4.05 kB
metadata
license: apache-2.0
library_name: peft
tags:
  - axolotl
  - generated_from_trainer
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
model-index:
  - name: isafpr-tiny-llama-lora-sharegpt
    results: []

Built with Axolotl

See axolotl config

axolotl version: 0.4.1

base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: false
# I'm training on 4090 GPUs
# so I'm using 4-bit precision to save on memory
load_in_4bit: true
strict: false

data_seed: 42
seed: 42

datasets:
  - path: data/sharegpt_isaf_press_releases_ft_train.jsonl
    type: sharegpt
    conversation: alpaca
dataset_prepared_path:
val_set_size: 0.1
output_dir: ./outputs/tiny-llama/lora-out-sharegpt
hub_model_id: strickvl/isafpr-tiny-llama-lora-sharegpt

sequence_len: 4096
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: isaf_pr_ft
wandb_entity: strickvl
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

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

warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"

isafpr-tiny-llama-lora-sharegpt

This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0507

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: 0.0002
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 2
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 16
  • total_eval_batch_size: 4
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 10
  • num_epochs: 4

Training results

Training Loss Epoch Step Validation Loss
1.7687 0.0270 1 1.7719
1.0632 0.2703 10 0.9033
0.1374 0.5405 20 0.1365
0.0763 0.8108 30 0.0942
0.0752 1.0608 40 0.0765
0.0764 1.3311 50 0.0680
0.0623 1.6014 60 0.0630
0.0596 1.8716 70 0.0593
0.0523 2.1216 80 0.0570
0.0514 2.3919 90 0.0543
0.0501 2.6622 100 0.0528
0.0475 2.9324 110 0.0515
0.0525 3.1824 120 0.0511
0.0436 3.4527 130 0.0509
0.0508 3.7230 140 0.0507

Framework versions

  • PEFT 0.11.1
  • Transformers 4.41.1
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1