Update app.py

app.py

@@ -6,12 +6,14 @@ api = gr.Interface.load("models/bigscience/bloom")
 
 def complete_with_gpt(text):
     # Use the last 50 characters of the text as context
-    return text[:-50] + api(text[-50:])
+    # return text[:-50] + api(text[-50:])
+    # Use the last 100 characters of the text as context
+    return text[:-100] + api(text[-100:])
 
 
 with gr.Blocks() as demo:
     with gr.Row():
-        textbox = gr.Textbox(placeholder="Type here and press enter...", lines=21)
+        textbox = gr.Textbox(placeholder="Type here and press enter...", lines=14)
         with gr.Column():
             btn = gr.Button("Generate")
 
@@ -19,9 +21,10 @@ with gr.Blocks() as demo:
 
     with gr.Row():
         gr.Markdown("""
-# Big Science creates 176 Billion Parameter Large Language Model 
+        
+# Big Science and Huggingface create 176 Billion Parameter Transformer Large Language Model 
 
-## Bloom Is Setting New Record for Most Performant and Efficient AI Model for Science Ever!
+## Bloom Is Setting A New Record for Most Performant and Efficient AI Model for Science Ever!
 
 Bloom stands for:
 B: Big Science
@@ -30,7 +33,7 @@ O: Open Science
 O: Open Access
 M: Multi Lingual Language Model
 
-1. Video Playlist to Check it out:  https://www.youtube.com/playlist?list=PLHgX2IExbFouqnsIqziThlPCX_miiDq14
+1. [Video Playlist](https://www.youtube.com/playlist?list=PLHgX2IExbFouqnsIqziThlPCX_miiDq14)
 2. Summary of Important Models and Sizes:
 
 # Model Sizes to Date
@@ -54,8 +57,6 @@ DistilBERT|66 million
 
 3. Background Information on ChatGPT, Bloom from BigScience on HuggingFace Platform, and RLHF DeepRL and One to Few Shot Learning and Generators:
 
-
-
 # ChatGPT Datasets:
 1. WebText
 2. Common Crawl
@@ -64,43 +65,41 @@ DistilBERT|66 million
 5. Toronto Books Corpus
 6. OpenWebText
 
-# Comparison to BigScience Model:
+# Comparison to BigScience Model - Big Science - How to get started
 
-# Big Science - How to get started
+Big Science is a 176B parameter ML model trained on a set of datasets for Natural Language processing, and many other tasks that are not yet explored..  
+Below is the set of the papers, models, links, and datasets around big science which promises to be the best, 
+most recent large model of its kind benefitting all science pursuits.
 
-Big Science is a 176B parameter new ML model that was trained on a set of datasets for Natural Language processing, and many other tasks that are not yet explored..  Below is the set of the papers, models, links, and datasets around big science which promises to be the best, most recent large model of its kind benefitting all science pursuits.
-
-# Model: https://huggingface.co/bigscience/bloom
+# [Model](https://huggingface.co/bigscience/bloom)
 
 # Papers: 
-1. BLOOM: A 176B-Parameter Open-Access Multilingual Language Model https://arxiv.org/abs/2211.05100
-2. Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism https://arxiv.org/abs/1909.08053
-3. 8-bit Optimizers via Block-wise Quantization https://arxiv.org/abs/2110.02861
-4. Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation https://arxiv.org/abs/2108.12409
-5. https://huggingface.co/models?other=doi:10.57967/hf/0003
-6. 217 Other Models optimizing use of bloom via specialization: https://huggingface.co/models?other=bloom
+1. BLOOM: A 176B-Parameter Open-Access Multilingual Language Model [Paper](https://arxiv.org/abs/2211.05100)
+2. Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism [Paper](https://arxiv.org/abs/1909.08053)
+3. 8-bit Optimizers via Block-wise Quantization [Paper](https://arxiv.org/abs/2110.02861)
+4. Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation [Paper](https://arxiv.org/abs/2108.12409)
+5. [Paper](https://huggingface.co/models?other=doi:10.57967/hf/0003)
+6. 217 Other Models optimizing use of bloom via specialization: [Paper](https://huggingface.co/models?other=bloom)
 
 # Datasets
-1. Universal Dependencies: https://paperswithcode.com/dataset/universal-dependencies
-2. WMT 2014: https://paperswithcode.com/dataset/wmt-2014
-3. The Pile: https://paperswithcode.com/dataset/the-pile
-4. HumanEval: https://paperswithcode.com/dataset/humaneval
-5. FLORES-101: https://paperswithcode.com/dataset/flores-101
-6. CrowS-Pairs: https://paperswithcode.com/dataset/crows-pairs
-7. WikiLingua: https://paperswithcode.com/dataset/wikilingua
-8. MTEB: https://paperswithcode.com/dataset/mteb
-9. xP3: https://paperswithcode.com/dataset/xp3
-10. DiaBLa: https://paperswithcode.com/dataset/diabla
+1. [Universal Dependencies](https://paperswithcode.com/dataset/universal-dependencies)
+2. [WMT 2014](https://paperswithcode.com/dataset/wmt-2014)
+3. [The Pile](https://paperswithcode.com/dataset/the-pile)
+4. [HumanEval](https://paperswithcode.com/dataset/humaneval)
+5. [FLORES-101](https://paperswithcode.com/dataset/flores-101)
+6. [CrowS-Pairs](https://paperswithcode.com/dataset/crows-pairs)
+7. [WikiLingua](https://paperswithcode.com/dataset/wikilingua)
+8. [MTEB](https://paperswithcode.com/dataset/mteb)
+9. [xP3](https://paperswithcode.com/dataset/xp3)
+10. [DiaBLa](https://paperswithcode.com/dataset/diabla)
 
 # Deep RL ML Strategy
-
 1. Language Model Preparation, Human Augmented with Supervised Fine Tuning
 2. Reward Model Training with Prompts Dataset Multi-Model Generate Data to Rank
 3. Fine Tuning with Reinforcement Reward and Distance Distribution Regret Score
 4. Proximal Policy Optimization Fine Tuning
 
 # Variations - Preference Model Pretraining
-
 1. Use Ranking Datasets Sentiment - Thumbs Up/Down, Distribution
 2. Online Version Getting Feedback
 3. OpenAI - InstructGPT - Humans generate LM Training Text