117 lines
3.8 KiB
Markdown
117 lines
3.8 KiB
Markdown
---
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library_name: transformers
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extra_gated_heading: Access Gemma on Hugging Face
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extra_gated_prompt: >-
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To access Gemma on Hugging Face, you’re required to review and agree to
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Google’s usage license. To do this, please ensure you’re logged-in to Hugging
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Face and click below. Requests are processed immediately.
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extra_gated_button_content: Acknowledge license
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license: other
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license_name: gemma-terms-of-use
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license_link: https://ai.google.dev/gemma/terms
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base_model:
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- google/gemma-7b
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datasets:
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- vicgalle/alpaca-gpt4
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---
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# Gemmalpaca-7B
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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.
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It's mostly a test to see how fine-tuning works with Gemma models on a well-known dataset.
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## 🔍 Applications
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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.
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## 🏆 Evaluation
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### Nous
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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).
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| Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
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|---|---:|---:|---:|---:|---:|
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| [**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** |
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| [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 |
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| [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 |
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| [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 |
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## 🧩 Configuration
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It was trained using [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) with the following configuration.
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```yaml
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base_model: alpindale/gemma-7b
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model_type: AutoModelForCausalLM
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tokenizer_config: philschmid/gemma-tokenizer-chatml
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tokenizer_type: AutoTokenizer
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tokenizer_use_fast: true
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load_in_8bit: false
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load_in_4bit: true
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strict: false
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datasets:
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- path: vicgalle/alpaca-gpt4
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type: alpaca
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dataset_prepared_path:
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val_set_size: 0.01
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output_dir: ./out
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sequence_len: 2048
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sample_packing: true
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pad_to_sequence_len: true
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adapter: qlora
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lora_model_dir:
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lora_r: 32
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lora_alpha: 64
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lora_dropout: 0.05
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lora_target_linear: true
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wandb_project: axolotl
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wandb_entity:
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wandb_watch:
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wandb_name:
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wandb_log_model:
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gradient_accumulation_steps: 2
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micro_batch_size: 4
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num_epochs: 3
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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learning_rate: 0.0002
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train_on_inputs: false
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group_by_length: false
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bf16: auto
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fp16:
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tf32: false
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gradient_checkpointing: true
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early_stopping_patience:
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resume_from_checkpoint:
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local_rank:
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logging_steps: 1
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xformers_attention:
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flash_attention: true
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warmup_steps: 10
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evals_per_epoch: 10
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eval_table_size:
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eval_table_max_new_tokens: 128
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saves_per_epoch: 1
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debug:
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deepspeed:
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weight_decay: 0.1
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fsdp:
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fsdp_config:
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special_tokens:
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```
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[<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) |