Generating Summaries for Large Documents with Llama2 using Hugging Face and Langchain
Introduction
In my debut blog, I’m delving into the world of automatic summarization, a vital tool in our information-driven era. The introduction of Meta’s LLama2 model has revolutionized this domain, making summarization more accessible and efficient than ever. In this tutorial, I’ll unveil how LLama2, in tandem with Hugging Face and LangChain — a framework for creating applications using large language models — can swiftly generate concise summaries, even for substantial documents such as intricate patient reports. Let’s embark on this exploration of the art of summarization, powered by cutting-edge technology.
Before You Begin: Access and Resources
To begin your journey into generating concise summaries, ensure you’re equipped with the essential tools and resources. The synergy between Hugging Face, LLama2, and LangChain is a testament to the potent text processing capabilities available today. Here’s your roadmap to getting started:
- Access Requests: Start by requesting access from both Hugging Face and Meta, using the same email ID for both platforms. This ensures a seamless and integrated experience.
- Access Links: Utilize the following access links:
- For Hugging Face: meta-llama/Llama-2–7b-hf
- For Meta: meta.com/resources/models-and-libraries
Remember to log in with the same email ID used during your access requests.
3. Access Token: To interact with Hugging Face’s models, you’ll need an access token. Go to Hugging Face’s token page. Note down your token, as you’ll require it in the upcoming steps.
4. Hardware Considerations: Efficient text processing relies on powerful hardware. For example, if you’re using Google Colab, consider utilizing a high-end processor like the A100 GPU. The hardware requirements can vary based on the size and complexity of your documents.
5. Downloading and Running Models Locally: You also have the option to download and run these models locally. In the email from Meta where you receive access, there will be instructions on how to download the models. If you have a good GPU instance, you can run them locally with ease. If you’d like me to create a blog post on this topic, let me know. I’d be happy to write an article describing the whole process.
Initiating the Summarization Quest: Hugging Face, Llama2, and Langchain
Crafting concise summaries for extensive documents is within reach through the synergy of Hugging Face, Llama2, and Langchain. Let’s get started:
Install Essential Packages:
!pip install -q transformers langchain accelerateHugging Face Cli Login
In your Google Colab notebook or Windows cmd, enter:
huggingface-cli loginhuggingface-cli login command is crucial for authenticating your Hugging Face account, granting you access to a world of pre-trained models.
Input the token you generated earlier. After that, press ‘y’
Import Packages:
To kickstart our journey into generating insightful summaries for large documents, we need to ensure we have the right tools at our disposal. Begin by importing the necessary packages that form the foundation of our solution:
import torch
import transformers
from transformers import AutoTokenizer
from langchain import LLMChain, HuggingFacePipeline, PromptTemplateModel and Tokenization:
At the heart of our summarization process lies the selection of a powerful language model and efficient tokenization. Let’s set the stage by configuring the model and tokenizer:
model = "meta-llama/Llama-2-7b-chat-hf"
tokenizer = AutoTokenizer.from_pretrained(model)In the code above, we pick the meta-llama/Llama-2–7b-chat-hf model. This model, used with Hugging Face’s HuggingFacePipeline, is key to our summarization work. The tokenizer, made from the model, turns text into a format the model can handle well. With these parts ready, we’re set to unleash AI-powered summarization.
Choosing the Right Model: Exploring Llama2 Variants
Our pursuit of powerful summaries leads to the meta-llama/Llama-2–7b-chat-hf model — a Llama2 version with 7 billion parameters. However, the Llama2 landscape is vast. The Meta-Llama Model Repository hosts various versions, each with unique parameters. For those craving top-notch summarization, models with more parameters hold appeal, promising refined summaries. Yet, remember, more parameters mean more computing power required. Your decision hinges on your needs. Explore the repository, assess Llama2 options, and consider document complexity, hardware, and accuracy. Choosing thoughtfully ensures your model matches your goals, balancing performance and efficiency.
Creating the Summarization Pipeline
At the core of our summarization method is a well-built pipeline that combines AI skills with language expertise. Let’s examine the parts that come together to shape this process:
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
max_length=3000,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id
)In the code above, we create a smart pipeline using Hugging Face’s Transformers library. This pipeline is like a conductor for summarization. The chosen model, tokenizer, and different settings — like max_length and eos_token_id — work together for this AI magic.
In the code snippet, notice the max_length set to 3000. It helps prevent issues if the document is too long for the AI. Adjusting this max_length according to your document’s length keeps the AI’s insights clear and avoids errors.
By setting the max_length parameter right, you harmonize the document’s length with the AI’s brevity. This creates a smooth summarization process without errors.
Fine-Tuning with Temperature: Customizing the Output
To infuse our generated summaries with a touch of finesse, we turn our attention to the parameter `temperature`. This nuanced parameter impacts the diversity and randomness of our AI-generated outputs.
llm = HuggingFacePipeline(pipeline = pipeline, model_kwargs = {'temperature':0})In the code above, we use the HuggingFacePipeline to shape our summarization process. By adding model_kwargs, we tweak the outcome with the temperature setting. A temperature of 0 gives direct summaries, while higher values add some randomness.
When you set the temperature, think about your readers and situation. For formal documents, a lower value might be fitting, while a higher value could be engaging for creative pieces. This customization lets you make AI-generated summaries match your style and vibe just right.
Crafting a Guiding Template: Summarization Made Seamless
To channel the power of AI into generating succinct and comprehensive summaries, we introduce the concept of a guiding template. This template serves as a blueprint to direct the summarization process and ensure the encapsulation of key points.
template = """
Write a summary of the following text delimited by triple backticks.
Return your response which covers the key points of the text.
```{text}```
SUMMARY:
"""The template structure is ingenious in its simplicity. It first provides clear instructions, encapsulated within triple backticks, signaling the text you intend to summarize. Your role is to craft a response that distills the essence of the content. The concluding touch is the dedicated "SUMMARY:" section that punctuates your generated summaries.
This template not only streamlines the summarization process but also provides a framework to encapsulate the document's core points. It serves as your artistic palette, allowing you to harness AI's potential while weaving your human touch into the fabric of each summary.
The Power of a Guiding Template
In the world of AI summarization, your template isn’t just a blueprint — it’s the captain’s wheel that guides the AI’s insights. Crafting a precise template is key. Random choices won’t yield aligned results. For deeper insights into effective template design, explore Sophia Yang, Ph.D.’s article on Best Practices in Prompt Engineering. It’s a guiding light to optimize AI summaries through strategic template construction.
The right template seamlessly merges AI potential with your human touch, creating summaries that engage and captivate.
Elevating Summarization with Langchain
Once you’ve established the core pipeline, it’s time to elevate your summarization capabilities with Langchain. With the prowess of Langchain, generating insightful summaries becomes an attainable goal. Utilize PromptTemplate to structure your summarization process and LLMChain to seamlessly connect Langchain with your pipeline. Here's how you can achieve this synergy:Now that our groundwork is laid, we venture into the world of prompts-the vital link between human intent and AI-generated summaries.
prompt = PromptTemplate(template=template, input_variables=["text"])
llm_chain = LLMChain(prompt=prompt, llm=llm)The PromptTemplate shapes the dialogue, adapting dynamically to your input. Paired with the LLMChain, this synergy fuses human creativity with AI precision, forging summaries that captivate.
Together, these components are the key, unlocking a harmonious partnership between your vision and the AI's prowess—a partnership that crafts summaries resonating seamlessly with your audience.
The Challenge: Summarizing a 4000-Word Patient Report
Our quest to showcase AI-powered summarization led us to a unique challenge: requesting ChatGPT to generate an extensive 4000-word patient report. Below is the detailed patient report, meticulously crafted by ChatGPT:
text = """
Patient Name: John Doe
Date of Birth: January 15, 1975
Gender: Male
Medical Record Number: 123456789
Date of Assessment: August 18, 2023
I. Chief Complaint:
The patient presents with complaints of persistent fatigue, unexplained weight loss, and intermittent abdominal pain over the past few months. He reports a gradual decrease in appetite and occasional nausea. The patient is seeking medical evaluation to determine the underlying cause of his symptoms.
II. Medical History:
The patient has a history of hypertension managed with medication for the past five years. He underwent an appendectomy in his late twenties and had a hernia repair surgery a decade ago. The patient reports a family history of diabetes on his maternal side.
III. Review of Systems:
General: The patient reports fatigue, unexplained weight loss of approximately 10 pounds over three months, and a decreased appetite.
Gastrointestinal: The patient experiences intermittent abdominal pain, predominantly in the right upper quadrant, without a clear trigger. He reports occasional nausea, and denies vomiting, diarrhea, or changes in bowel habits.
Cardiovascular: The patient's blood pressure has been well controlled with medication. He denies chest pain, palpitations, or shortness of breath.
Respiratory: The patient denies cough, wheezing, or shortness of breath.
Musculoskeletal: No significant joint pain or limitations in mobility reported.
Neurological: The patient denies headaches, dizziness, or changes in vision.
Psychological: The patient mentions occasional stress due to work-related factors but denies symptoms of depression or anxiety.
IV. Physical Examination:
Vital Signs: Blood pressure is 130/80 mmHg, heart rate is 78 beats per minute, respiratory rate is 16 breaths per minute, and temperature is 98.6°F (37°C).
General: The patient appears fatigued but alert and oriented to person, place, and time. He appears to have lost weight since his last visit.
Abdominal Examination: There is tenderness on palpation in the right upper quadrant of the abdomen. No palpable masses or organomegaly noted. Bowel sounds are normal.
Cardiovascular Examination: Regular rate and rhythm with no murmurs or abnormal sounds.
Respiratory Examination: Clear breath sounds bilaterally, no wheezing or crackles noted.
Neurological Examination: No focal neurological deficits observed.
V. Diagnostic Investigations:
Complete Blood Count (CBC): Within normal limits.
Comprehensive Metabolic Panel (CMP): Slight decrease in albumin levels.
Liver Function Tests (LFTs): Mild elevation in liver enzymes (AST and ALT).
Abdominal Ultrasound: No evidence of gallstones or other abnormalities. Liver appears slightly enlarged.
CT Scan of the Abdomen: Reveals a mass in the liver, approximately 5 cm in diameter. The mass appears to be enhancing on contrast.
VI. Assessment and Plan:
Based on the patient's symptoms, physical examination, and diagnostic investigations, the following assessment and plan have been formulated:
Assessment:
Unexplained weight loss and fatigue.
Right upper quadrant abdominal pain.
Elevated liver enzymes and an enlarging liver mass.
Plan:
Further Evaluation: Given the presence of an enlarging liver mass and elevated liver enzymes, the patient will be referred to a gastroenterologist for further evaluation, including possible liver biopsy.
Oncology Consultation: Given the possibility of malignancy, an oncology consultation will be sought to determine the nature of the liver mass and develop an appropriate treatment plan.
Symptom Management: The patient's abdominal pain will be managed with pain relievers as needed. Anti-nausea medication will be prescribed to alleviate nausea symptoms.
Nutritional Support: The patient's decreased appetite and weight loss will be addressed with dietary counseling and nutritional support to ensure adequate calorie intake.
Psychological Support: Given the patient's stress and anxiety related to his symptoms, psychological support and counseling will be offered to address emotional well-being.
VII. Follow-Up:
The patient is scheduled for a follow-up appointment in two weeks to discuss the results of further investigations, consultations, and to address any ongoing symptoms. The patient is advised to monitor his symptoms and report any significant changes or new symptoms before the next visit.
VIII. Prognosis and Discussion:
The presence of an enlarging liver mass raises concerns about potential malignancy. Further diagnostic testing and consultations with specialists are essential to determine the nature of the mass and develop an appropriate treatment plan. The patient's overall prognosis will be influenced by the nature of the liver mass and the success of treatment interventions. Close collaboration between the patient, medical team, and specialists will guide the management of his condition and optimize his chances of recovery.
IX. Patient Education:
The patient and his family will receive comprehensive education about the diagnostic process, potential treatment options, and the importance of adhering to medical recommendations. Emphasis will be placed on the need for regular follow-up appointments and open communication with the medical team to ensure the best possible outcomes.
X. Conclusion:
This patient report outlines the comprehensive health assessment of John Doe, a male patient presenting with unexplained weight loss, fatigue, and abdominal pain. The assessment encompasses a thorough medical history, review of systems, physical examination findings, diagnostic investigations, assessment, and a detailed plan for further evaluation and management. The collaboration between healthcare professionals and specialists will be crucial in guiding the patient's journey toward an accurate diagnosis, effective treatment, and optimal health outcomes.
"""This comprehensive report mirrors the complexity of real-world medical assessments. It’s the ideal input document for our summarization experiment, demonstrating how AI can distill vast amounts of information while retaining the crucial details.
As we dive into the experiment, observe how AI harnesses its summarization prowess to transform lengthy narratives into succinct insights.
Unveiling AI-Generated Summaries
With our input document in hand, it’s time to witness the magic of AI-driven summarization in action.
print(llm_chain.run(text))The code snippet above triggers the AI summarization process using the llm_chain we meticulously assembled. As the AI combs through the extensive patient report, it condenses the content into a concise summary that captures the essence of the narrative.
🚀Finally, the Generated Summary🚀
At last, we arrive at the culmination of our exploration — the generated summary derived from the patient report:
“John Doe, a 48-year-old male patient, presented with persistent fatigue, unexplained weight loss, and intermittent abdominal pain over the past few months. His medical history includes hypertension, appendectomy, and hernia repair surgery. The patient’s vital signs were normal, and his physical examination revealed tenderness in the right upper quadrant of the abdomen. Diagnostic investigations, including a complete blood count, comprehensive metabolic panel, liver function tests, abdominal ultrasound, and CT scan of the abdomen, revealed elevated liver enzymes and an enlarging liver mass. Based on the assessment and plan, the patient will be referred to a gastroenterologist for further evaluation and management, and an oncology consultation will be sought to determine the nature of the liver mass. The patient’s prognosis will be influenced by the nature of the liver mass and the success of treatment interventions.”
This definitive AI-generated summary attests to the transformative power of LLama2, condensing intricate narratives into concise, informative insights. While compact, it encapsulates the vital components of the patient report, showcasing how AI-driven summarization bridges the divide between exhaustive documents and impactful takeaways.
Prioritizing Data Privacy and Security
As you traverse the realm of text summarization, data privacy and security are paramount. Hugging Face acknowledges these concerns and offers safeguards for your data. For those concerned about data privacy, Hugging Face provides a Business Associate Addendum or GDPR data processing agreement through the Inference Endpoint enterprise plan. Discover more about their robust security standards here. Additionally, for those who seek greater control over their data and processes, you can opt to download models and run them locally, thereby enhancing your control and independence while ensuring the utmost privacy and security. If you’d like me to create a blog post on this topic, let me know. I’d be happy to write an article describing the whole process.
Conclusion
Thank you for joining me on this journey. Abstractive summarization using Llama2, Hugging Face, and Langchain offers an efficient way to swiftly comprehend lengthy documents. We’ve learned to seamlessly blend our insights with LLM capabilities to create impactful summaries. Dive in and begin your summarization adventures! If you found this article helpful, a thumbs-up would be much appreciated. Please feel free to share any feedback — your insights drive our ongoing evolution.