---
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
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license: other
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
base_model:
- google/gemma-7b
datasets:
- vicgalle/alpaca-gpt4
---

![image/webp](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/uwPjZeV-JQwKWrI7nHg4w.webp)

# Gemmalpaca-7B

This is gemma-7b model supervised fine-tuned on the [vicgalle/alpaca-gpt4](https://huggingface.co/datasets/vicgalle/alpaca-gpt4) dataset. It outperforms gemma-7b-it, Google's chat version, on Nous' benchmark suite.

It's mostly a test to see how fine-tuning works with Gemma models on a well-known dataset.

## 🔍 Applications

This model has a context length of 8k. I recommend using it with the Alpaca chat template and NOT the Gemma Instruct template (works perfectly with LM Studio). You also want to add `</s>` as a stop token.

## 🏆 Evaluation

### Nous

Gemmalpaca-7B outperforms gemma-7b and gemma-7b-it on Nous' benchmark suite (evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval)). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard).

| Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
|---|---:|---:|---:|---:|---:|
| [**mlabonne/Gemmalpaca-7B**](https://huggingface.co/mlabonne/Gemmalpaca-7B) [📄](https://gist.github.com/mlabonne/61622c46e53914a16e11be89d078f66c) | **34.45** | **21.6** | **40.87** | **44.85** | **30.49** |
| [google/gemma-2b](https://huggingface.co/google/gemma-2b) [📄](https://gist.github.com/mlabonne/7df1f238c515a5f63a750c8792cef59e) | 34.26 | 22.7 | 43.35 | 39.96 | 31.03 |
| [google/gemma-7b](https://huggingface.co/google/gemma-7b) [📄](https://gist.github.com/mlabonne/5f9855d341c3b11f775348ecb4fd8cf1) | 33.56 | 20.64 | 38.49 | 46.61 | 28.51 |
| [google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it) [📄](https://gist.github.com/mlabonne/0fb752dc3c5b578fff87a73c56a16d7a) | 33.53 | 21.33 | 40.84 | 41.7 | 30.25 |

## 🧩 Configuration

It was trained using [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) with the following configuration.

```yaml
base_model: alpindale/gemma-7b
model_type: AutoModelForCausalLM
tokenizer_config: philschmid/gemma-tokenizer-chatml
tokenizer_type: AutoTokenizer
tokenizer_use_fast: true

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: vicgalle/alpaca-gpt4
    type: alpaca

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: 2
micro_batch_size: 4
num_epochs: 3
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_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:
```

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)