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Co-authored-by: SFconvertbot <SFconvertbot@users.noreply.huggingface.co>
Co-authored-by: peter szemraj <peter szemraj@users.noreply.huggingface.co>

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README.md

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+---
+license:
+- apache-2.0
+- bsd-3-clause
+tags:
+- summarization
+- summary
+- booksum
+- long-document
+- long-form
+- tglobal-xl
+- XL
+- 8bit
+- quantized
+datasets:
+- kmfoda/booksum
+metrics:
+- rouge
+inference: false
+pipeline_tag: summarization
+---
+
+
+# long-t5-tglobal-xl-16384-book-summary: 8-bit quantized version
+
+<a href="https://colab.research.google.com/gist/pszemraj/c19e32baf876deb866c31cd46c86e893/long-t5-xl-accelerate-test.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a>
+
+> [!IMPORTANT]
+> As of [this discussion](https://huggingface.co/pszemraj/long-t5-tglobal-base-16384-book-summary/discussions/23) we found issues with long-t5 models >= 4.23.0 - please use `pip install transformers==4.22.0` to ensure good performance with this model
+
+
+This is an 8-bit quantized version of the `pszemraj/long-t5-tglobal-xl-16384-book-summary` model, 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 (vs original 12 GB)
+- Enables low-RAM loading, making it easier to use in memory-limited environments like Colab
+- Requires `bitsandbytes` - AFAIK at time of writing, only works on GPU
+
+
+## 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.
+  - The XL checkpoint typically generates summaries that are considerably better from a human evaluation perspective.

config.json

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+  "architectures": [
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+  ],
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+  "num_heads": 32,
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+  "output_past": true,
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+    "load_in_8bit": true
+  },
+  "relative_attention_max_distance": 128,
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+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.28.1",
+  "use_cache": true,
+  "vocab_size": 32128
+}

generation_config.json

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+  "no_repeat_ngram_size": 3,
+  "num_beams": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.28.1"
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