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import streamlit as st
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st.set_page_config(
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layout="wide",
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initial_sidebar_state="auto"
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)
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st.markdown("""
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<style>
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.main-title {
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font-size: 36px;
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color: #4A90E2;
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font-weight: bold;
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text-align: center;
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}
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.sub-title {
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font-size: 24px;
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color: #4A90E2;
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margin-top: 20px;
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}
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.section {
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background-color: #f9f9f9;
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padding: 15px;
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border-radius: 10px;
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margin-top: 20px;
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}
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.section h2 {
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font-size: 22px;
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color: #4A90E2;
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}
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.section p, .section ul {
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color: #666666;
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}
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.link {
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color: #4A90E2;
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text-decoration: none;
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}
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</style>
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""", unsafe_allow_html=True)
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st.markdown('<div class="main-title">Chat and Conversational LLMs (Facebook Llama-2)</div>', unsafe_allow_html=True)
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st.markdown("""
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<div class="section">
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<p>Facebook's Llama-2 is a cutting-edge family of large language models (LLMs) designed to excel in a variety of conversational tasks. With models ranging from 7 billion to 70 billion parameters, Llama-2 has been fine-tuned specifically for dialogue use cases, making it one of the most powerful and versatile models available for chat and conversational AI.</p>
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<p>Llama-2 models have demonstrated superior performance across multiple benchmarks, often outperforming other open-source models and rivaling some of the best closed-source models like ChatGPT and PaLM. These models are capable of handling complex, context-rich conversations with a high degree of accuracy and coherence.</p>
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</div>
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""", unsafe_allow_html=True)
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st.markdown('<div class="sub-title">Understanding the Llama-2 Transformer</div>', unsafe_allow_html=True)
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st.markdown("""
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<div class="section">
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<h2>Llama-2: The Transformer Architecture</h2>
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<p>Llama-2 is based on the transformer architecture, a deep learning model that has revolutionized the field of natural language processing. The transformer model employs a mechanism called self-attention, which allows it to weigh the importance of different words in a sentence relative to each other. This enables the model to capture long-range dependencies in text, making it highly effective for understanding and generating human-like text.</p>
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<p>The Llama-2 model family builds on this architecture, incorporating enhancements that improve its ability to handle longer contexts and generate more accurate and coherent responses. The model is particularly well-suited for dialogue and conversational applications, where understanding context and maintaining coherence over multiple turns of conversation is crucial.</p>
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</div>
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""", unsafe_allow_html=True)
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st.markdown('<div class="sub-title">Performance and Benchmarks</div>', unsafe_allow_html=True)
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st.markdown("""
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<div class="section">
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<p>Llama-2-Chat models have been rigorously tested against a variety of benchmarks to assess their performance in dialogue and conversational tasks. The results have shown that Llama-2 outperforms other open-source chat models on most benchmarks, demonstrating its effectiveness in generating accurate, relevant, and contextually appropriate responses.</p>
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<p>In human evaluations, Llama-2-Chat has been found to be on par with some of the leading closed-source models in terms of helpfulness and safety. This makes it a highly reliable option for developers looking to implement conversational AI in their applications.</p>
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</div>
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""", unsafe_allow_html=True)
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st.markdown('<div class="sub-title">Implementing Llama-2 for Conversational AI</div>', unsafe_allow_html=True)
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st.markdown("""
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<div class="section">
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<p>The following is an example of how to implement a Llama-2 model for generating responses in a conversational AI application. We use the Llama-2 model with a simple Spark NLP pipeline to generate responses to user input.</p>
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</div>
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""", unsafe_allow_html=True)
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st.code('''
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from sparknlp.base import *
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from sparknlp.annotator import *
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from pyspark.ml import Pipeline
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from pyspark.sql.functions import col, expr
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documentAssembler = DocumentAssembler() \\
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.setInputCol("text") \\
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.setOutputCol("documents")
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llama2 = LLAMA2Transformer \\
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.pretrained("llama_2_7b_chat_hf_int4") \\
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.setMaxOutputLength(50) \\
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.setDoSample(False) \\
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.setInputCols(["documents"]) \\
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.setOutputCol("generation")
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pipeline = Pipeline().setStages([documentAssembler, llama2])
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data = spark.createDataFrame([["what are your thoughts about the new monkeypox virus"]]).toDF("text")
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result = pipeline.fit(data).transform(data)
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result.select("generation.result").show(truncate=False)
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''', language='python')
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st.text("""
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+------------------------------------------------+
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|generation.result |
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+------------------------------------------------+
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|Monkeypox is a rare disease that has been ... |
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+------------------------------------------------+
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""")
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st.markdown('<div class="sub-title">Choosing the Right Llama-2 Model</div>', unsafe_allow_html=True)
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st.markdown("""
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<div class="section">
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<p>Llama-2 models are available in various sizes and configurations, depending on the specific needs of your application. For conversational AI, it is important to select a model that balances performance with resource efficiency. The model used in the example, "llama_2_7b_chat_hf_int4," is optimized for chat applications and is a good starting point for many use cases.</p>
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<p>For more complex tasks or larger-scale deployments, you may consider using one of the larger Llama-2 models, such as the 13B or 70B parameter variants, which offer greater accuracy and contextual understanding.</p>
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<p>Explore the available models on the <a class="link" href="https://sparknlp.org/models?annotator=LLAMA2Transformer" target="_blank">Spark NLP Models Hub</a> to find the one that fits your needs.</p>
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</div>
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""", unsafe_allow_html=True)
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st.markdown('<div class="sub-title">References</div>', unsafe_allow_html=True)
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st.markdown("""
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<div class="section">
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<ul>
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<li><a class="link" href="https://ai.facebook.com/" target="_blank">Facebook AI Research</a>: Learn more about Facebook's AI initiatives</li>
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<li><a class="link" href="https://sparknlp.org/models?annotator=LLAMA2Transformer" target="_blank">Spark NLP Model Hub</a>: Explore Llama-2 models</li>
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<li><a class="link" href="https://huggingface.co/facebook/llama" target="_blank">Hugging Face Model Hub</a>: Explore Llama-2 models</li>
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<li><a class="link" href="https://github.com/facebookresearch/llama" target="_blank">GitHub</a>: Access the Llama-2 repository and contribute</li>
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<li><a class="link" href="https://ai.facebook.com/blog/introducing-llama-2" target="_blank">Llama-2 Blog Post</a>: Detailed insights from the developers</li>
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</ul>
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</div>
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""", unsafe_allow_html=True)
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st.markdown('<div class="sub-title">Community & Support</div>', unsafe_allow_html=True)
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st.markdown("""
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<div class="section">
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<ul>
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<li><a class="link" href="https://sparknlp.org/" target="_blank">Official Website</a>: Documentation and examples</li>
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<li><a class="link" href="https://join.slack.com/t/spark-nlp/shared_invite/zt-198dipu77-L3UWNe_AJ8xqDk0ivmih5Q" target="_blank">Slack</a>: Live discussion with the community and team</li>
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<li><a class="link" href="https://github.com/JohnSnowLabs/spark-nlp" target="_blank">GitHub</a>: Bug reports, feature requests, and contributions</li>
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<li><a class="link" href="https://medium.com/spark-nlp" target="_blank">Medium</a>: Spark NLP articles</li>
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<li><a class="link" href="https://www.youtube.com/channel/UCmFOjlpYEhxf_wJUDuz6xxQ/videos" target="_blank">YouTube</a>: Video tutorials</li>
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</ul>
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</div>
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""", unsafe_allow_html=True)
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st.markdown('<div class="sub-title">Quick Links</div>', unsafe_allow_html=True)
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st.markdown("""
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<div class="section">
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<ul>
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<li><a class="link" href="https://sparknlp.org/docs/en/quickstart" target="_blank">Getting Started</a></li>
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<li><a class="link" href="https://nlp.johnsnowlabs.com/models" target="_blank">Pretrained Models</a></li>
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<li><a class="link" href="https://github.com/JohnSnowLabs/spark-nlp/tree/master/examples/python/annotation/text/english" target="_blank">Example Notebooks</a></li>
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<li><a class="link" href="https://sparknlp.org/docs/en/install" target="_blank">Installation Guide</a></li>
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</ul>
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</div>
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""", unsafe_allow_html=True)
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