metadata
base_model: google/gemma-2-9b
library_name: peft
license: gemma
tags:
- generated_from_trainer
model-index:
- name: lora-out
results: []
See axolotl config
axolotl version: 0.4.1
base_model: google/gemma-2-9b
sequence_len: 1024
# base model weight quantization
load_in_8bit: true
# load_in_4bit: true
# attention implementation
flash_attention: true
# finetuned adapter config
adapter: lora
lora_model_dir:
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_modules_to_save: # required when adding new tokens to LLaMA/Mistral
- embed_tokens
- lm_head
# if training fails, uncomment above
# for details, see https://github.com/huggingface/peft/issues/334#issuecomment-1561727994
###
# Dataset Configuration: sqlqa
###
# datasets:
# - path: data.jsonl
# type: alpaca
datasets:
- path: public_train_data.jsonl
ds_type: json
type:
field_instruction: instruction
field_input: input
field_output: output
format: |-
[INST] {instruction}
{input} [/INST]
chat_template: gemma
tokens:
- "[INST]"
- " [/INST]"
- "[QL]"
- " [/QL]"
- "[EXPLANATION]"
- " [/EXPLANATION]"
# dataset formatting config
special_tokens:
pad_token: <|end_of_text|>
val_set_size: 0.05
###
# Training Configuration
###
# masks the input messages so that the model learns and understands the language w/o being reliant on the input
train_on_inputs: false
# random seed for better reproducibility
seed: 117
# optimizer config
optimizer: adamw_bnb_8bit
learning_rate: 0.0001
lr_scheduler: cosine
num_epochs: 4
micro_batch_size: 4
gradient_accumulation_steps: 1
warmup_steps: 10
# axolotl saving config
dataset_prepared_path: last_run_prepared
output_dir: ./lora-out
# logging and eval config
logging_steps: 1
eval_steps: 0.05
# training performance optimization config
bf16: auto
tf32: false
gradient_checkpointing: true
###
# Miscellaneous Configuration
###
# when true, prevents over-writing the config from the CLI
strict: false
# "Don't mess with this, it's here for accelerate and torchrun" -- axolotl docs
local_rank:
# WANDB
wandb_mode:
wandb_project:
wandb_watch:
wandb_name:
wandb_run_id:
# Multi-GPU
# deepspeed: /root/axolotl/deepspeed_configs/zero3_bf16.json
# deepspeed: zero3_bf16.json
# deepspeed: /workspace/axolotl/deepspeed_configs/zero2.json
deepspeed:
fsdp:
fsdp_config:
lora-out
This model is a fine-tuned version of google/gemma-2-9b on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0077
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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 117
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 16
- 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: 10
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.7925 | 0.0385 | 1 | 2.0412 |
| 1.6872 | 0.2308 | 6 | 1.6089 |
| 0.6967 | 0.4615 | 12 | 0.6328 |
| 0.3327 | 0.6923 | 18 | 0.2711 |
| 0.1784 | 0.9231 | 24 | 0.1733 |
| 0.1136 | 1.1538 | 30 | 0.1190 |
| 0.0891 | 1.3846 | 36 | 0.0850 |
| 0.0746 | 1.6154 | 42 | 0.0626 |
| 0.0522 | 1.8462 | 48 | 0.0465 |
| 0.033 | 2.0769 | 54 | 0.0282 |
| 0.0333 | 2.3077 | 60 | 0.0225 |
| 0.0171 | 2.5385 | 66 | 0.0203 |
| 0.0172 | 2.7692 | 72 | 0.0144 |
| 0.0095 | 3.0 | 78 | 0.0119 |
| 0.0088 | 3.2308 | 84 | 0.0099 |
| 0.0054 | 3.4615 | 90 | 0.0089 |
| 0.0073 | 3.6923 | 96 | 0.0085 |
| 0.0059 | 3.9231 | 102 | 0.0077 |
Framework versions
- PEFT 0.13.0
- Transformers 4.45.1
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.20.0