237 lines
6.8 KiB
Markdown
237 lines
6.8 KiB
Markdown
---
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license: other
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library_name: transformers
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datasets:
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- vicgalle/alpaca-gpt4
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extra_gated_heading: Access Gemma on Hugging Face
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extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
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agree to 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_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-2b
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model-index:
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- name: Gemmalpaca-2B
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: acc_norm
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value: 48.72
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Gemmalpaca-2B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc_norm
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value: 71.36
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Gemmalpaca-2B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU (5-Shot)
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type: cais/mmlu
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config: all
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 36.3
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Gemmalpaca-2B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: mc2
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value: 41.24
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Gemmalpaca-2B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 65.59
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Gemmalpaca-2B
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GSM8k (5-shot)
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type: gsm8k
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 10.69
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mlabonne/Gemmalpaca-2B
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name: Open LLM Leaderboard
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---
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# Gemmalpaca-2B
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This is gemma-2b model supervised fine-tuned on the [vicgalle/alpaca-gpt4](https://huggingface.co/datasets/vicgalle/alpaca-gpt4) dataset. It outperforms gemma-2b-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. It turned out better than expected. :)
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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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## ⚡ Quantized models
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* **GGUF**: https://huggingface.co/mlabonne/Gemmalpaca-2B-GGUF
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## 🏆 Evaluation
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### Nous
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Gemmalpaca-2B outperforms gemma-2b and gemma-2b-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-2B](https://huggingface.co/mlabonne/Gemmalpaca-2B) [📄](https://gist.github.com/mlabonne/4b638752fc3227df566f9562064cb864) | 38.39 | 24.48 | 51.22 | 47.02 | 30.85 |
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| [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) [📄](https://gist.github.com/mlabonne/db0761e74175573292acf497da9e5d95) | 36.1 | 23.76 | 43.6 | 47.64 | 29.41 |
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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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### [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_mlabonne__Gemmalpaca-2B)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |45.65|
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|AI2 Reasoning Challenge (25-Shot)|48.72|
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|HellaSwag (10-Shot) |71.36|
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|MMLU (5-Shot) |36.30|
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|TruthfulQA (0-shot) |41.24|
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|Winogrande (5-shot) |65.59|
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|GSM8k (5-shot) |10.69|
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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-2b
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model_type: GemmaForCausalLM
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tokenizer_type: GemmaTokenizer
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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: 4
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micro_batch_size: 2
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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:
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warmup_steps: 10
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evals_per_epoch: 4
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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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bos_token: <s>
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eos_token: </s>
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unk_token: <unk>
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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)
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