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