Try TeapotLLM for Reliable Q&A, RAG, and Info Extraction

TeapotLLM: A Lightweight, Hallucination-Resistant Language Model

Text generation models are powerful tools for research and applications, leveraging architecture, training, and extensive datasets to achieve remarkable capabilities. TeapotAI's open-source TeapotLLM exemplifies a high-performing, resource-efficient model. This 800M parameter language model, fine-tuned on synthetic data, excels in low-resource environments like smartphones and CPUs. Its functionality focuses on Q&A, Retrieval-Augmented Generation (RAG), and information extraction within a given context.

Key Features and Capabilities:

• Retrieval Augmented Generation (RAG): TeapotLLM can be fine-tuned for RAG using a custom embedding model, enabling it to answer questions by extracting information from provided documents.
• Hallucination Resistance: Trained on a synthetic dataset, TeapotLLM avoids generating fabricated information, ensuring responses are grounded in the provided context.
• Pydantic Data Extraction: The accompanying TeapotAI Python package includes a Pydantic-based data extraction function, enabling efficient and accurate structured data retrieval from text.

Model Architecture and Training:

TeapotLLM is based on a transformer architecture, specifically fine-tuned from Flan-T5-base using a synthetic dataset generated by DeepSeek-V3. Its encoder-decoder structure processes input and output sequences, converting input text into a latent representation that the decoder uses to generate task-specific responses. The model incorporates standard transformer principles like multi-head self-attention, feed-forward networks, and layer normalization, contributing to its strong contextual understanding.

Implementation and Use Cases:

TeapotLLM is readily deployable for various tasks:

• Q&A: Simple question answering based on provided context.
• RAG: Answering questions using multiple documents as context.
• Structured Data Extraction: Extracting information from text using Pydantic models for precise data formatting.

The following code snippets illustrate its usage:

(Q&A Example):

from teapotai import TeapotAI
context = """The Eiffel Tower is 330 meters tall."""
teapot_ai = TeapotAI()
answer = teapot_ai.query("How tall is the Eiffel Tower?", context=context)
print(answer)

(RAG Example):

from teapotai import TeapotAI
documents = ["Document 1...", "Document 2..."]
teapot_ai = TeapotAI(documents=documents)
answer = teapot_ai.chat([{"role": "user", "content": "My question..." }])
print(answer)

(Structured Data Extraction Example):

from teapotai import TeapotAI
from pydantic import BaseModel, Field
class Apartment(BaseModel): rent: float = Field(...)
teapot_ai = TeapotAI()
extracted_data = teapot_ai.extract(Apartment, context="Apartment rent: $2000")
print(extracted_data)

Real-World Applications:

TeapotLLM finds applications in various fields:

• AI Chatbots: Providing context-aware, accurate responses.
• Content Generation: Summarizing documents and generating reports.
• Data Extraction: Processing structured data from diverse sources like legal and financial documents.

Conclusion:

TeapotLLM stands out as a lightweight, accurate, and hallucination-resistant language model. Its efficiency, coupled with its strong performance in Q&A, RAG, and structured data extraction, makes it a valuable asset for numerous AI applications. Its open-source nature further enhances its accessibility and potential for community contributions.

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