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gemma-2-27b-it-dolly-15k

Text Generation
PEFT
Safetensors
gemma2
axolotl
Generated from Trainer
conversational
4-bit precision
bitsandbytes
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metadata
library_name: peft
license: gemma
base_model: google/gemma-2-27b-it
tags:
  - axolotl
  - generated_from_trainer
model-index:
  - name: gemma-2-27b-it-dolly-15k
    results: []
datasets:
  - databricks/databricks-dolly-15k
pipeline_tag: text-generation

Built with Axolotl

See axolotl config

axolotl version: 0.5.2

base_model: google/gemma-2-27b-it
hub_model_id: kweinmeister/gemma-2-27b-it-dolly-15k

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: databricks/databricks-dolly-15k
    type:
      field_instruction: instruction       
      field_input: context
      field_output: response
val_set_size: 0.05

sequence_len: 2048
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

adapter: qlora
lora_model_dir:
lora_r: 32
lora_alpha: 64
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: gemma-2-27b-it-dolly-15k
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.0001

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: false

warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed: deepspeed_configs/zero2.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
output_dir: "/mnt/disks/gcs/training/runs/google--gemma-2-27b-it-20250101-192231/out/"
dataset_prepared_path: "/mnt/disks/gcs/training/datasets"

gemma-2-27b-it-dolly-15k

This model is a fine-tuned version of google/gemma-2-27b-it on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.5560

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: 42
  • distributed_type: multi-GPU
  • num_devices: 2
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • total_eval_batch_size: 8
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 10
  • num_epochs: 1

Training results

Training Loss Epoch Step Validation Loss
4.2291 0.0244 1 2.1246
2.1928 0.2683 11 1.6858
1.742 0.5366 22 1.5769
1.7213 0.8049 33 1.5560

Framework versions

  • PEFT 0.13.2
  • Transformers 4.46.3
  • Pytorch 2.4.1+cu124
  • Datasets 3.1.0
  • Tokenizers 0.20.3