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--- |
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license: gemma |
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library_name: transformers |
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tags: |
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- sft |
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- generated_from_trainer |
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base_model: google/gemma-7b |
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model-index: |
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- name: gemma_ft_quote |
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results: [] |
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pipeline_tag: text-generation |
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datasets: |
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- Abirate/english_quotes |
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language: |
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- en |
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widget: |
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- text: 'Quote: With great power comes' |
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example_title: Example 1 |
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- text: 'Quote: Hasta la vista baby' |
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example_title: Example 2 |
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- text: 'Quote: Elementary, my dear watson.' |
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example_title: Example 3 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Gemma_ft_Quote |
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This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on the [english quote](https://huggingface.co/datasets/Abirate/english_quotes) dataset using [LoRA](https://arxiv.org/abs/2106.09685). |
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It is based on the example provided by google [here](https://huggingface.co/google/gemma-7b/blob/main/examples/notebook_sft_peft.ipynb). |
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The notebook used to fine-tune the model can be found [here](https://colab.research.google.com/drive/1OMvXuK77X7yxofrhQHERUkrn3NZORXFp?usp=sharing) |
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## Model description |
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The model can complete popular quotes given to it and add the author of the quote. For example, Given the qoute below: |
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``` |
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Quote: With great power comes |
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``` |
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The model would complete the quote and add the author of the quote: |
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``` |
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Quote: With great power comes great responsibility. Author: Ben Parker. |
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``` |
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Given a complete Quoute the model would add the author: |
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``` |
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Quote: I'll be back. Author: Arnold Schwarzenegger. |
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``` |
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## Usage |
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The model can be used with [transformers](https://huggingface.co/docs/transformers/en/index) library. Here's an example of loading the model |
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in 4 bit quantization mode: |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig |
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model_id = "Eteims/gemma_ft_quote" |
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bnb_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_compute_dtype=torch.bfloat16 |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map="cuda:0") |
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``` |
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This code would easily run in a free colab tier. |
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After loading the model you can use it for inference: |
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```python |
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text = "Quote: Elementary, my dear watson." |
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device = "cuda:0" |
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inputs = tokenizer(text, return_tensors="pt").to(device) |
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outputs = model.generate(**inputs, max_new_tokens=20) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during fine-tuning: |
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- learning_rate: 0.0002 |
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- train_batch_size: 1 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 4 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 2 |
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- training_steps: 10 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- PEFT 0.8.2 |
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- Transformers 4.38.1 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
