update README and add LICENSE

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README.md

@@ -1,3 +1,66 @@
 ---
 license: apache-2.0
+datasets:
+- tatsu-lab/alpaca
 ---
+
+## Flan-UL2-Alpaca
+
+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).
+
+### Resource Considerations
+
+A goal of this project was to produce this model with a limited budget demonstrating the ability train a robust, commercially viable 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:
+- Pricing: $1.302/hour
+- OS: Ubuntu 22.10 x64
+- 6 vCPUs
+- 60 GB CPU RAM
+- 40 GB GPU RAM (1/2 x A100)
+
+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:
+- Epochs: 2
+- Learning Rate: 1e-5
+- Batch Size: 1
+- Gradient Accumulation Steps: 8
+- 8 Bit Mode: Yes
+
+
+### Why?
+
+Rapid recent advancements in the natural language processing (NLP) space have been extraordinary. Large Language Models (LLMs) like Meta's LLaMA are getting a lot of attention with their remarkable generative abilities however, many people are looking at the implications of these projects and looking for ways to leverage the technology in a commercial setting. Unfortunately, many LLMs (ie LLaMA, Vicuna) are limited by their licensing, restricting opportunities for usage within businesses and products.
+
+To address this issue, the entirely open-source [Flan-UL2 model](https://huggingface.co/google/flan-ul2), built by Google on the [Flan-T5](https://arxiv.org/abs/2210.11416) encoder-decoder framework, is an excellent alternative to LLMs with more restrictive licensing. Flan-UL2 is accessible for commercial applications and fine-tuned on academic NLP tasks, providing exceptional performance in comparison to models of similar size across various benchmarks. Additionally, with a receptive field of 2048 token is suitable for a number of LLM tasks including [Retrieval Augmented Generation (RAG)](https://arxiv.org/abs/2005.11401).
+
+### Usage
+
+```
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from peft import PeftModel, PeftConfig
+
+prompt = "Write a story about an alpaca that went to the zoo."
+
+peft_model_id = 'coniferlabs/flan-ul2-alpaca-lora'
+config = PeftConfig.from_pretrained(peft_model_id)
+model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, device_map="auto", load_in_8bit=True)
+model = PeftModel.from_pretrained(model, peft_model_id, device_map={'': 0})
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+model.eval()
+
+tokenized_text = tokenizer.encode(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(input_ids=tokenized_text, parameters={"min_length": 10, "max_length": 250})
+tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+###
+```
+
+
+### Flan-UL2 Training Results
+
+| Epoch | Train Loss | Eval Loss  |
+|-------|------------|------------|
+| 1     | 12102.7285 |  2048.0518 |
+| 2     | 9318.9199  |  2033.5337 |
+
+![image](assets/training_loss.png)
+
+Loss Trendline: y = -1.1302001815753724e-05x + 0.73000991550589