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--- |
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license: apache-2.0 |
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datasets: |
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- tatsu-lab/alpaca |
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--- |
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## Flan-UL2-Alpaca |
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Model weights are from epoch 0. |
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This [Github repository](https://github.com/ConiferLabsWA/flan-ul2-alpaca) contains code for leveraging the [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) synthetic dataset to fine tune the [Flan-UL2](https://huggingface.co/google/flan-ul2) model, leveraging recent advances in instruction tuning. The Flan UL2 model has been shown to outperform Flan-T5 XXL on a number of metrics and has a 4x improvement in receptive field (2048 vs 512). |
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### Resource Considerations |
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A goal of this project was to produce this model with a limited budget demonstrating the ability train a robust LLM using systems available to even small businesses and individuals. This had the added benefit of personally saving me money as well :). To achieve this a server was rented on [vultr.com](vultr.com) with the following pricing/specs: |
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- Pricing: $1.302/hour |
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- OS: Ubuntu 22.10 x64 |
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- 6 vCPUs |
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- 60 GB CPU RAM |
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- 40 GB GPU RAM (1/2 x A100) |
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To dramatically reduce memory footprint and compute requirements [Low Rank Adaption(LoRA)](https://huggingface.co/docs/diffusers/training/lora) was used as opposed to finetuning the entire network. Additionally, the Flan-UL2 model was loaded and trained in 8 bit mode, also greatly reducing memory requirements. Finally, a batch size of 1 was used with 8 gradient accumulation steps. Here is a list of training parameters used: |
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- Epochs: 2 |
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- Learning Rate: 1e-5 |
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- Batch Size: 1 |
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- Gradient Accumulation Steps: 8 |
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- 8 Bit Mode: Yes |
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### Usage |
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``` |
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from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
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from peft import PeftModel, PeftConfig |
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prompt = "Write a story about an alpaca that went to the zoo." |
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peft_model_id = 'coniferlabs/flan-ul2-alpaca-lora' |
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config = PeftConfig.from_pretrained(peft_model_id) |
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model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, device_map="auto", load_in_8bit=True) |
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model = PeftModel.from_pretrained(model, peft_model_id, device_map={'': 0}) |
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tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) |
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model.eval() |
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tokenized_text = tokenizer.encode(prompt, return_tensors="pt").to("cuda") |
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outputs = model.generate(input_ids=tokenized_text, parameters={"min_length": 10, "max_length": 250}) |
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tokenizer.batch_decode(outputs, skip_special_tokens=True) |
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### |
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``` |
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### Flan-UL2 Training Results |
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| Epoch | Train Loss | Eval Loss | |
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|-------|------------|------------| |
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| 1 | 12102.7285 | 2048.0518 | |
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| 2 | 9318.9199 | 2033.5337 | |
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Loss Trendline: y = -1.1302001815753724e-05x + 0.73000991550589 |
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