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.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
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+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
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LICENSE

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+BSD 3-Clause License
+
+Copyright (c) 2023, synkathairo
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

README.md

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+---
+title: Flan T5 Large Gradio
+emoji: 🐨
+colorFrom: indigo
+colorTo: green
+sdk: gradio
+sdk_version: 3.29.0
+app_file: app.py
+pinned: false
+license: bsd-3-clause
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# flan-t5-large-gradio
+A [gradio](https://gradio.app/) frontend for [Google's Flan-T5 Large](https://huggingface.co/google/flan-t5-large) language model, can also be adjusted for other sizes.
+
+The gradio frontend runs in the browser, and displays a chat-like interface for interacting with the language model.
+
+## To run:
+
+Install the dependencies: gradio, transformers, sentencepiece
+
+Recommended to install: safetensors
+
+Optionally also install: accelerate
+
+Run with: `python app.py`
+
+Optionally run with: `accelerate launch app.py`
+(this may or may not speed up your execution)
+
+## To use other size models
+
+Modify lines 4-6, comment/uncomment to select a different model. Note that each model has different requirements for storage/RAM, the code as presently configured assumes sufficient RAM to load the entire model.
+
+Additional info on loading large models is on the [Hugging Face website](https://huggingface.co/blog/accelerate-large-models), see [also](https://huggingface.co/docs/accelerate/v0.11.0/en/big_modeling)

app.py

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+from transformers import T5Tokenizer, T5ForConditionalGeneration
+import gradio as gr
+
+# model_name = "google/flan-t5-small"
+# model_name = "google/flan-t5-base"
+model_name = "google/flan-t5-large"
+# model_name = "google/flan-t5-xl"
+
+default_max_length = 200
+
+print("Using `{}`.".format(model_name))
+
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+print("T5Tokenizer loaded from pretrained.")
+
+model = T5ForConditionalGeneration.from_pretrained(model_name, device_map="auto")
+print("T5ForConditionalGeneration loaded from pretrained.")
+
+
+def inference(max_length, input_text, history=[]):
+    input_ids = tokenizer(input_text, return_tensors="pt").input_ids
+    outputs = model.generate(input_ids, max_length=max_length, bos_token_id=0)
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    history.append((input_text, result))
+    return history, history
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        gr.Markdown(
+            "<h1>Demo of {}</h1><p>See more at Hugging Face: <a href='https://huggingface.co/{}'>{}</a>.</p>".format(
+                model_name, model_name, model_name
+            )
+        )
+        max_length = gr.Number(
+            value=default_max_length, label="maximum length of response"
+        )
+
+    chatbot = gr.Chatbot(label=model_name)
+    state = gr.State([])
+
+    with gr.Row():
+        txt = gr.Textbox(
+            show_label=False, placeholder="Enter text and press enter"
+        ).style(container=False)
+
+    txt.submit(fn=inference, inputs=[max_length, txt, state], outputs=[chatbot, state])
+
+demo.launch()

requirements.txt

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+gradio
+transformers
+sentencepiece
+safetensors
+accelerate