See axolotl config
axolotl version: 0.4.1
adapter: qlora
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
bf16: false
dataset_prepared_path: null
datasets:
- path: mhenrichsen/alpaca_2k_test
type: alpaca
debug: null
deepspeed: null
early_stopping_patience: null
eval_sample_packing: false
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
learning_rate: 0.0002
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lora_target_modules: null
lr_scheduler: cosine
micro_batch_size: 2
model_type: LlamaForCausalLM
num_epochs: 4
optimizer: paged_adamw_32bit
output_dir: ./outputs/qlora-out
pad_to_sequence_len: false
resume_from_checkpoint: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 4096
special_tokens: null
strict: false
tf32: null
tokenizer_type: LlamaTokenizer
train_on_inputs: false
val_set_size: 0.05
wandb_entity: null
wandb_log_model: null
wandb_name: null
wandb_project: null
wandb_watch: null
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
outputs/qlora-out
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: 1.2295
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 |
|---|---|---|---|
| 1.5605 | 0.0042 | 1 | 1.5265 |
| 1.1485 | 0.2526 | 60 | 1.2386 |
| 1.1249 | 0.5053 | 120 | 1.2167 |
| 1.3675 | 0.7579 | 180 | 1.2130 |
| 1.3449 | 1.0105 | 240 | 1.1995 |
| 1.1825 | 1.2632 | 300 | 1.2074 |
| 0.9782 | 1.5158 | 360 | 1.2060 |
| 1.2063 | 1.7684 | 420 | 1.1994 |
| 0.9614 | 2.0211 | 480 | 1.1929 |
| 1.0084 | 2.2737 | 540 | 1.2140 |
| 1.1655 | 2.5263 | 600 | 1.2174 |
| 1.1503 | 2.7789 | 660 | 1.2198 |
| 0.9577 | 3.0316 | 720 | 1.2164 |
| 0.9943 | 3.2842 | 780 | 1.2286 |
| 1.0043 | 3.5368 | 840 | 1.2289 |
| 1.128 | 3.7895 | 900 | 1.2295 |
Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.1.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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