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tinyllama-1.1B_alpaca_2k_lora

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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: tinyllama-1.1B_alpaca_2k_lora
    results: []

Built with Axolotl

See axolotl config

axolotl version: 0.4.0

# Adapted from https://github.com/OpenAccess-AI-Collective/axolotl/blob/main/examples/tiny-llama/lora.yml
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: true
load_in_4bit: false
strict: false

datasets:
  - path: mhenrichsen/alpaca_2k_test
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/lora-out
hub_model_id: lucasvw/tinyllama-1.1B_alpaca_2k_lora

wandb_project: tinyllama-1.1B_alpaca_2k_lora
wandb_entity: lucasvw

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:

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:

tinyllama-1.1B_alpaca_2k_lora

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: 1.2132

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
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 8
  • 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
1.4615 0.08 1 1.4899
1.3851 0.24 3 1.4860
1.3667 0.48 6 1.4396
1.2684 0.72 9 1.3410
1.2274 0.96 12 1.2938
1.2519 1.16 15 1.2810
1.2263 1.4 18 1.2534
1.1355 1.6400 21 1.2357
1.2697 1.88 24 1.2260
1.1492 2.08 27 1.2217
1.1531 2.32 30 1.2216
1.1951 2.56 33 1.2184
1.1118 2.8 36 1.2158
1.1514 3.04 39 1.2127
1.1893 3.24 42 1.2124
1.1014 3.48 45 1.2115
1.1892 3.7200 48 1.2132

Framework versions

  • PEFT 0.10.0
  • Transformers 4.40.2
  • Pytorch 2.1.2+cu118
  • Datasets 2.19.1
  • Tokenizers 0.19.1