Unveiling Salesforce's Exogen: Efficient 7B LLM Model for Summarization
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Today on Abhishek Thakur's channel, we delved into the world of Salesforce's latest marvel, Exogen. This 7B LLM powerhouse is no ordinary model, boasting training on an 8K input sequence length. It's like the standard Lama model, but with a twist - trained on a whopping 1.5 trillion tokens. What sets Exogen apart is its Apache 2.0 license, allowing commercial use, and the availability of both code base and models. The 7B 4K and 7B 8K base models are also up for grabs under the same license, making it a versatile tool for various applications.
The team explored the realm of instruction fine-tuned models, cautioning that these are strictly for research purposes. Leveraging the tick token tokenizer from OpenAI, these models operate akin to the Lama model. Moving on to the practical side, a simple summarizer application was crafted using Torch, Radio, and Transformers. By loading the tokenizer and model, the stage was set for a seamless summarization process. With parameters like max length and temperature in play, the model could churn out summaries based on input text, showcasing its adaptability and efficiency.
The application was put to the test, generating concise and accurate summaries from text inputs. The NextGen 8K model emerged as a game-changer, overcoming limitations by training on an extensive 1.5 trillion tokens. It delivered results that were not just good but exceptional, proving its mettle in summarization tasks. Viewers were encouraged to engage by asking questions, leaving comments, and showing support through subscriptions and shares. As the video wrapped up, it was evident that Exogen and its capabilities had left a lasting impression, setting the stage for future explorations in the world of AI models and applications.
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Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
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Tips on quantizing via GPTQ and optimizing for inference latency
Can it summarize conversation transcripts?
Hardware specifications for running this
Quality comparison with WizardLM and Vicuna 1.3
Maximum number of input tokens and confusion about 1.5T tokens
Multilingual capabilities
Error message and fix related to adding device_map='auto'
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