See axolotl config
axolotl version: 0.4.0
# use google/gemma-7b if you have access
base_model: google/gemma-2b
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
#lora_model_dir:
load_in_8bit: false
load_in_4bit: true
strict: false
# huggingface repo
datasets:
- path: ./python-oasst/cleaned-dataset.jsonl
type: oasst
val_set_size: 0.20
output_dir: ./out1v
adapter: lora
lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
gptq: false
sequence_len: 4096
sample_packing: false
pad_to_sequence_len: true
wandb_project: gemma-2b-it
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 2
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/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
out1v
This model is a fine-tuned version of google/gemma-2b on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1243
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: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1045
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.9865 | 0.0 | 1 | 2.2808 |
| 1.1033 | 0.25 | 2613 | 1.1338 |
| 1.0035 | 0.5 | 5226 | 1.1308 |
| 1.0089 | 0.75 | 7839 | 1.1272 |
| 0.9816 | 1.0 | 10452 | 1.1238 |
| 0.8727 | 1.25 | 13065 | 1.1270 |
| 0.9951 | 1.5 | 15678 | 1.1254 |
| 1.01 | 1.75 | 18291 | 1.1242 |
| 1.0677 | 2.0 | 20904 | 1.1243 |
Framework versions
- PEFT 0.10.0
- Transformers 4.40.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0
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