Update README.md

README.md

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 ---
-license: bsd-3-clause
+license:
+  - apache-2.0
+  - bsd-3-clause
+tags:
+  - summarization
+  - summary
+  - booksum
+  - long-document
+  - long-form
+  - tglobal-xl
+  - XL
+datasets:
+  - kmfoda/booksum
+metrics:
+  - rouge
+inference: false
 ---
+
+
+# long-t5-tglobal-xl-8b: 8-bit quantized version
+
+This is an 8-bit quantized version of the `pszemraj/long-t5-tglobal-xl-16384-book-summary` model, called `long-t5-tglobal-xl-8b`. The model has been compressed using `bitsandbytes` and can be loaded with low memory usage.
+
+Refer to the [original model](https://huggingface.co/pszemraj/long-t5-tglobal-xl-16384-book-summary) for all details about the model architecture and training process. For more information on loading 8-bit models, refer to the `4.28.0` [release information](https://github.com/huggingface/transformers/releases/tag/v4.28.0) and the [example repository](https://huggingface.co/ybelkada/bloom-1b7-8bit).
+
+- The total size of the model is only ~3.5 GB, much smaller than the original size.
+- This allows for low-RAM loading, making it easier to use in memory-limited environments.
+
+## Basic Usage
+
+To use the model, install or upgrade `transformers`, `accelerate`, and `bitsandbytes`. Make sure to have `transformers>=4.28.0` and `bitsandbytes>0.37.2`.
+
+```bash
+pip install -U -q transformers bitsandbytes accelerate
+```
+
+Load the model with `AutoTokenizer` and `AutoModelForSeq2SeqLM`:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+model_name = "pszemraj/long-t5-tglobal-xl-16384-book-summary-8bit"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+```
+
+## More information about long-t5-tglobal-xl-16384-book-summary
+
+- This is an 8-bit quantized version of `pszemraj/long-t5-tglobal-xl-16384-book-summary`.
+- It generalizes reasonably well to academic and narrative text, producing high-quality summaries.
+- The XL checkpoint is used, resulting in even better summaries from a human evaluation perspective.
+- A simple example/use case with the base model on ASR can be found [here](https://huggingface.co/pszemraj/long-t5-tglobal-xl-16384-book-summary/tree/main/examples/asr).
+- A proof-of-concept example using the infamous Navy Seals copypasta demonstrates the model's ability to generate summaries from even short text inputs.