Hugging Face's logo

LoneStriker
/
OrcaGemma-2B-6.0bpw-h6-exl2

Text Generation
Transformers
Safetensors
gemma
text-generation-inference
Inference Endpoints
Model card Files Community
OrcaGemma-2B-6.0bpw-h6-exl2 / README.md
LoneStriker's picture
LoneStriker
Upload folder using huggingface_hub
fe4e6ef verified
preview code
|
raw
history blame contribute delete
3.51 kB
metadata
library_name: transformers
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: >-
  To access Gemma on Hugging Face, you’re required to review and agree to
  Google’s usage license. To do this, please ensure you’re logged-in to Hugging
  Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
license: other
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
base_model:
  - google/gemma-2b
datasets:
  - Open-Orca/SlimOrca-Dedup

image/webp

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:

Built with Axolotl