See axolotl config
axolotl version: 0.4.0
# use google/gemma-7b if you have access
base_model: google/gemma-7b-it
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: true
strict: false
# huggingface repo
datasets:
- path: ./python-oasst/chunk_1.jsonl
type: oasst
val_set_size: 0.1
output_dir: ./out
adapter: qlora
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
sequence_len: 4096
sample_packing: false
pad_to_sequence_len: true
wandb_project: gemma-7b-it
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 6
micro_batch_size: 4
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: true
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_ratio: 0.1
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed: deepspeed_configs/zero1.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
out
This model is a fine-tuned version of google/gemma-7b-it on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1905
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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 6
- total_train_batch_size: 96
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 9
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 5.0474 | 0.01 | 1 | 5.9279 |
| 1.221 | 0.26 | 24 | 1.2960 |
| 1.1167 | 0.51 | 48 | 1.1657 |
| 1.0702 | 0.77 | 72 | 1.1372 |
| 0.9553 | 1.02 | 96 | 1.1292 |
| 0.9294 | 1.28 | 120 | 1.1301 |
| 0.9603 | 1.54 | 144 | 1.1254 |
| 0.8544 | 1.79 | 168 | 1.1276 |
| 0.826 | 2.05 | 192 | 1.1462 |
| 0.816 | 2.31 | 216 | 1.1500 |
| 0.7392 | 2.56 | 240 | 1.1446 |
| 0.7597 | 2.82 | 264 | 1.1469 |
| 0.6664 | 3.07 | 288 | 1.1908 |
| 0.6968 | 3.33 | 312 | 1.1842 |
| 0.7327 | 3.59 | 336 | 1.1899 |
| 0.7211 | 3.84 | 360 | 1.1905 |
Framework versions
- PEFT 0.9.0
- Transformers 4.39.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.18.0
- Tokenizers 0.15.0
- Downloads last month
- 0