Fine-Tuned
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axolotl version: 0.4.0
# use google/gemma-7b if you have access
#base_model: mhenrichsen/gemma-7b
base_model: google/gemma-7b
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
hub_model_id: MaziyarPanahi/gemma-7b-alpaca-52k-v0.1
hf_use_auth_token: true
load_in_8bit: false
load_in_4bit: true
strict: false
# huggingface repo
datasets:
- path: tatsu-lab/alpaca
type: alpaca
val_set_size: 0.1
output_dir: ./qlora-gemma-7b-alpaca
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: false
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 3
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
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:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
This model is a fine-tuned version of google/gemma-7b on the None dataset. It achieves the following results on the evaluation set:
PEFT
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM
model_id = "MaziyarPanahi/gemma-7b-alpaca-52k-v0.1"
config = PeftConfig.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained("google/gemma-7b")
model = PeftModel.from_pretrained(model, model_id)
Transformers
# Use a pipeline as a high-level helper
from transformers import pipeline
model_id = "MaziyarPanahi/gemma-7b-alpaca-52k-v0.1"
pipe = pipeline("text-generation", model=model_id)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
1.5395 | 0.0 | 1 | 1.4186 |
1.099 | 0.25 | 488 | 1.1994 |
1.2188 | 0.5 | 976 | 1.1751 |
1.0511 | 0.75 | 1464 | 1.1468 |
Base model
google/gemma-7b