Edit model card

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Log in or Sign Up to review the conditions and access this model content.

Built with Axolotl

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
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for dvdmrs09/gemma-7b-lora

Base model

google/gemma-7b
Finetuned
google/gemma-7b-it
Adapter
(73)
this model