gemmy-lora

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<details><summary>See axolotl config</summary>

axolotl version: 0.4.1

base_model: SillyTilly/google-gemma-2-9b
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

# huggingface repo
datasets:
  - path: MangoHQ/Claude-Data-Anon-Killed
    type: customgemma2
  - path: kalomaze/Opus_Instruct_25k
    type: customgemma2
  - path: kalomaze/Opus_Instruct_3k
    type: customgemma2
val_set_size: 0.0
output_dir: ./outputs/gemmy/

adapter: qlora
lora_r: 32
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true

sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

wandb_project: Ohashi-9B
wandb_entity:
wandb_watch:
wandb_name: Ohashi-9B-run1
wandb_log_model:


gradient_accumulation_steps: 32
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

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

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:
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

</details><br>

outputs/gemmy/

This model is a fine-tuned version of SillyTilly/google-gemma-2-9b on the None dataset.

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: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 32
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 50
• num_epochs: 2

Training results

Framework versions

• PEFT 0.11.1
• Transformers 4.44.0.dev0
• Pytorch 2.1.2+cu118
• Datasets 2.19.1
• Tokenizers 0.19.1