A Large Language Model (LLM) is a type of artificial intelligence model designed to understand and generate human language. Large language models can answer questions, write essays, summarise texts, translate languages, generate creative content, and even engage in conversation. Their ability to generate coherent and contextually relevant text is what makes them powerful tools for language-based applications.
GPUs are optimised for parallel processing, making them much faster than CPUs for tasks like training deep learning models, which involve extensive matrix calculations. In this Video you will find
MoE and MoA are two methodologies designed to enhance the performance of large language models (LLMs) by leveraging multiple models.
BNP Paribas has announced a multi-year partnership agreement with Mistral AI to leverage it’s commercial models.
Armand Ruiz has revealed the entirety of its comprehensive 6.48 TB dataset used to train Granite 13B.
“If you’re a student or an academic dreaming of making LLMs for Indian languages, stop wasting your time. You’re not going to make it.”
Perplexity AI had raised $15 million in its seed funding round when it was just a six-month old company, which it could have done in India.
Though there is definitely a need to work on other types of AI.
India needs lot of electricity in the future for AI? No, it just needs 1-bit LLMs.
Moreover, the recent Indian chatbot Hanooman, released by SML is also powered by IIT Bombay projects.
Setu and Sarvam AI have together created Sesame to be both domain and region-specific, by training it on custom data that is highly relevant to India’s BFSI sector.
One of the most important aspects of the xLSTM architecture is its flexible ratio of MLSTM and SLSTM blocks.
The model utilises an Auto-Regressive (AR) decoder that processes information sequentially, making it particularly adept at solving complex mathematical problems through logical reasoning.
The new release has added features to meet the needs of the AI and machine learning community.
The models come in 270M, 450M, 1.1B, and 3B parameters, both pre-trained and fine-tuned according to instructions.
Chinmaya Jena highlights that the problem with LLM is that there is no differentiation between the data plane and the control plane.
OpenAI proposes that when multiple instructions are presented to the model, lower-privileged instructions should only be followed if they are aligned with higher-privileged ones.
FlowMind significantly outperformed the GPT-Context-Retrieval baseline method, even without user feedback.
Despite its compact size, Phi-3-Mini boasts performance levels that rival larger models such as Mixtral 8x7B and GPT-3.5.
The model is available on Hugging Face.
Microsoft, Google, and Meta, have all been taking strides in this direction – making context length infinite.
Users can easily add customisations and optimizations to adapt models to specific use cases, including memory-efficient recipes that work on machines with single 24GB gaming GPUs.
The introduction of Feedback Attention Memory offers a new approach by adding feedback activations that feed contextual representation back into each block of sliding window attention.
In direct comparisons with Llama 2, MEGALODON demonstrates superior efficiency at a scale of 7 billion parameters and 2 trillion training tokens.
They have got nothing to lose.
Taught by Matt Robinson, head of product at Unstructured, the course is free for a limited time and takes about an hour to complete.
The modification to the Transformer attention layer supports continual pre-training and fine-tuning, facilitating the natural extension of existing LLMs to process infinitely long contexts.
“I can use my model very well, but how do I compare it with other Indian models? There was no uniformity,” Kolavi explained the need for building the leaderboard.
Along with this, Adithya S Kolavi, the founder of CreativeLab has also unveiled the indic_eval evaluation framework.
Swarna’s future endeavours include the expansion of datasets for Data Processing Ontology (DPO) in Telugu, a significant area that remains largely unexplored.
Tech mahindra news | Meta news | Semiconductor news | Mphasis news | Oracle news | Intel news | Deloitte news | Jio news | Job interview news | virtual internship news | IIT news | Certification news | Course news | Startup news | Leetcode news | claude news | Snowflake news | Python news | Microsoft news | AWS news
Discover how Cypher 2024 expands to the USA, bridging AI innovation gaps and tackling the challenges of enterprise AI adoption
© Analytics India Magazine Pvt Ltd & AIM Media House LLC 2024
