Latest Posts
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DBRX: A New State-of-the-Art Open LLM by Databricks
DBRX utilizes a transformer-based decoder-only architecture with a fine-grained Mixture-of-Experts (MoE) design. This means it uses a large number of smaller expert models to process different parts of the input, rather than relying on a single massive model.
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Fine-tune an Instruct model over raw text data
This experiment seeks to discover a lighter approach that navigates between the constraints of a 128K context window and the complexities of a model fine-tuned on billions of tokens, perhaps more in the realm of tens of millions of tokens. For a smaller-scale test, I’ll fine-tune Mistral’s 7B Instruct v0.2 model on The Guardian’s manage-frontend…
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Crew AI Tutorial
In the realm of artificial intelligence, the adoption of multi-agent systems (MAS) via crew ai represents a paradigm shift towards more dynamic and complex problem-solving capabilities. This blog dives into the essence of Multi Agent Systems, highlighting the necessity for such systems in today’s technological landscape and exploring the CrewAI framework as a possible solution.
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Fine Tuning LLMs on a Single Consumer Graphic Card
Unlock the secrets of fine-tuning large language models (LLMs) on a single consumer graphics card. Discover how to leverage your GPU for enhanced AI model performance without breaking the bank. Ideal for AI enthusiasts and developers seeking practical insights.
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LangChain Cheatsheet — All Secrets on a Single Page
Unlock the full potential of LangChain with our ultimate cheatsheet! Discover all the secrets and tips you need, condensed into a single, easy-to-navigate page. Save time and boost your efficiency today!
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TaCo: Enhancing Cross-Lingual Transfer for Low-Resource Languages in LLM
TaCo, short for Translation-Assisted Cross-Linguality, utilizes a clever combination of translation and chain-of-thought processes to efficiently train LLMs on new languages. This blog post delves into the details of TaCo, exploring its approach, datasets, training process, and evaluation results.
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RAFT: Finetuning LLM for Domain-Specific Knowledge
This blog post dives into RAFT (Retrieval-Augmented Fine-Tuning), a novel training recipe that enhances the ability of LLMs to perform well in domain-specific RAG tasks. We’ll explore the challenges of adapting LLMs to specialized domains, delve into the details of RAFT, and analyze its effectiveness through evaluation results and qualitative examples.
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Open Source Techniques Unlock Hidden Potential in LLM
Matt Shumer, CEO of Hyperwrite AI, introduced the “Claude Opus to Haiku” technique, which allows users to achieve Claude 3 Opus-level quality at a fraction of the cost and latency. This open-source method involves providing Claude 3 Opus with a task description and a single input-output example.
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MoonDream: A Tiny Vision Language Model for Edge Devices
MoonDream is a small, efficient vision language model (VLM) that can operate with less than 6GB of memory. This makes it ideal for use on edge devices in a variety of applications, such as: