Gemmalpaca-2B
This is gemma-2b model supervised fine-tuned on the Open-Orca/SlimOrca-Dedup dataset. It's not as good as mlabonne/Gemmalpaca-2B.
🏆 Evaluation
Nous
Gemmalpaca-2B outperforms gemma-2b but underperforms gemma-2b-it on Nous' benchmark suite (evaluation performed using LLM AutoEval). See the entire leaderboard here.
| Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
|---|---|---|---|---|---|
| mlabonne/Gemmalpaca-2B 📄 | 38.39 | 24.48 | 51.22 | 47.02 | 30.85 |
| google/gemma-2b-it 📄 | 36.1 | 23.76 | 43.6 | 47.64 | 29.41 |
| mlabonne/OrcaGemma-2B 📄 | 35.63 | 24.44 | 42.49 | 45.84 | 29.76 |
| google/gemma-2b 📄 | 34.26 | 22.7 | 43.35 | 39.96 | 31.03 |
🧩 Configuration
It was trained using Axolotl with the following configuration.
base_model: google/gemma-2b
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: Open-Orca/SlimOrca-Dedup
type: sharegpt
dataset_prepared_path:
val_set_size: 0.01
output_dir: ./out
sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true
adapter: qlora
lora_model_dir:
lora_r: 32
lora_alpha: 64
lora_dropout: 0.05
lora_target_linear: true
wandb_project: axolotl
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
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: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention:
warmup_steps: 10
evals_per_epoch: 10
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens: