Meta presents Layer Skip Enabling Early Exit Inference and Self-Speculative Decoding https://lnkd.in/eBFpbV4r We present LayerSkip, an end-to-end solution to speed-up inference of large language models (LLMs). First, during training we apply layer dropout, with low dropout rates for earlier layers and higher dropout rates for later layers, and an early exit loss where all transformer layers share the same exit. Second, during inference, we show that this training recipe increases the accuracy of early exit at earlier layers, without adding any auxiliary layers or modules to the model. Third, we present a novel self-speculative decoding solution where we exit at early layers and verify and correct with remaining layers of the model. Our proposed self-speculative decoding approach has less memory footprint than other speculative decoding approaches and benefits from shared compute and activations of the draft and verification stages. We run experiments on different Llama model sizes on different types of training: pretraining from scratch, continual pretraining, finetuning on specific data domain, and finetuning on specific task. We implement our inference solution and show speedups of up to 2.16x on summarization for CNN/DM documents, 1.82x on coding, and 2.0x on TOPv2 semantic parsing task.
Hamdi Amroun, PhD’s Post
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Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models✨ ⚡ Scaling up the model size to improve reasoning performance has its limits. There are more effective prompting methods, like single-query reasoning and multi-query reasoning, but both have some limitations ⚡ limitations: Single-query reasoning usually requires prior assumptions or relevant exemplars of the reasoning process. Multi-query reasoning is usually computationally intensive when finding a unique intrinsic structure underlying the reasoning process for each specific task. ⚡ To address these limitations, introduce Buffer of Thoughts (BoT) versatile thought-augmented reasoning framework aimed at enhancing the reasoning accuracy, efficiency, and robustness of LLMs across various tasks. ⚡introduce a meta-buffer, a lightweight library housing a series of universal high-level thoughts (thought-templates), distilled from different problem-solving processes. The buffer manager updates the meta-buffer. ⚡Buffer Manager: summarizes the high-level guidelines and thoughts gained from each problem-solving process, storing the critical, distilled knowledge in the form of thought-templates within the meta-buffer. ⚡Thought Template: The thought-template is stored in the meta-buffer and is obtained from various problem-solving processes by our buffer manager. BoT aims to provide a general reasoning approach for various tasks : Text Comprehension, Creative Language Generation, Common Sense Reasoning, Mathematical Reasoning, Code Programming, and Application Scheduling. ⚡Template Distillation: To extract a general thought-template, a three-step approach is used: 1) Core task summarization: identifying and describing the basic types and core challenges of problems. 2) Solution step description: summarizing the general steps for solving a problem. 3) General answering template: proposing a solution template that can be widely applied to similar problems. ⚡#Meta-Buffer: already possesses the necessary knowledge to solve this task and does not need to perform the update. This reduces the computational burden of template retrieval while ensuring the lightweight property of our meta-buffer. ⚡Advantages: Accuracy Improvement Reasoning Efficiency Model Robustness #datascience #machinelearning #llm
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Put #Graph + #LLM into real world? GraphGPT is not enough! HiGPT is here: 🔥 One Model, Any Heterogeneous Graph 🚀 Cross-domain Zero-shot Heterogeneous Graph Learning 🌟 Few-shot Training with Instruction Augmentation 🎉 1-shot Beat 60-shot with Graph In-Context Learning For more technical details, check out: 🏠 Website: https://lnkd.in/gTuAhD7Z 🧑💻 Github: https://lnkd.in/gigfbUzB 📝 Paper: https://lnkd.in/gpRRtccA 🤗 Model: https://lnkd.in/gNQ7FP5S HiGPT consists of: ✅ In-Context Heterogeneous Graph Tokenizer. To achieve adaptability in a wide range of heterogeneous graph scenarios with varying node and edge types, we introduce the in-context heterogeneous graph tokenizer. This tokenizer captures the diverse semantic relationships found in different heterogeneous graphs, providing a unified approach. To optimize performance and integrate the tokenizer seamlessly into the HiGPT framework, we employ pre-training with a lightweight text-graph contrastive alignment paradigm. ✅ Heterogeneous Graph Instruction-Tuning. We introduce a novel heterogeneous graph instruction-tuning framework that integrates inter-type and intra-type token matching tasks to fine-tune large language models (LLMs). Our framework specifically targets the enhancement of LLMs' understanding of both heterogeneous relation awareness and homogeneous relation awareness. ✅ Mixture-of-Thought Augmentation. Our approach introduces a novel mechanism for augmenting graph instructions, emphasizing the use of Mixture-of-Thought (MoT) combined with various prompting techniques. This integration enables us to generate a diverse and comprehensive set of informative task-specific instructions. By seamlessly incorporating these augmented graphinstructions into our framework, we anticipate that our model enhancement will effectively address the challenge of data sparsity. #GraphNeuralNetworks #LargeLanguageModels #InstructionTuning
Heterogeneous Graph Language Model
higpt-hku.github.io
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Large Language Models (LLMs) are inherently autoregressive and operate sequentially, predicting the next set of words based on the prior sequence iteratively to obtain the final outcome. During LLM inference, where the model generates text sequences, utilizing the computational power of GPUs often falls short due to the sequential nature. Is there a way to speed up this process? Common sense suggests using guesswork, essentially building sequences like t, t+1, t+2 on guesses and checking/correcting in case of failure. This approach is known as Speculative Decoding. To enhance guesswork, leveraging the prompt is effective. The prompt contains context, semantics, and structure, similar to how part of the answer in exam papers comes from the question, highlighting the importance of context and semantics. Another method is to use lookahead, inherent in LLM models, which generates a certain number of words/tokens ahead. Additionally, an efficient architecture for inference involves external help for guesswork, utilizing smaller language models or trained helpers. At the core of inference is token decoding, a highly sequential and memory-bound process. The bottleneck occurs with each forward pass, requiring the transfer of complete model parameters from High Bandwidth Memory to the accelerator cache. This single-token generation process underutilizes the arithmetic computation potential of modern accelerators, leading to inefficiency. Speculative decoding, as mentioned earlier, involves a smaller draft model generating token sequences at each step, refined by a larger model for accepted continuation. However, obtaining the right draft model presents a challenge. Another approach to expedite the process is to increase the number of decoding heads on top of the LLM backbone model. When effectively applied, this approach outperforms speculative decoding quickly. Enter MEDUSA, a method enhancing LLM inference by integrating additional decoding heads capable of predicting multiple tokens. These heads are fine-tuned in a parameter-efficient manner and can be added to any existing models. There is no requirement for a new model; MEDUSA offers easy and automatic integration into current LLM systems, including those in distributed environments, ensuring a user-friendly experience. For more information, refer here: https://lnkd.in/d_Udkb-H GitHub https://lnkd.in/dRg7x-iE #LanguageModels #DataScience #Innovation #business #data #research #indiastartups #complexity #ai #openai #llm #fintech #nbfc #gartner #bloomberg #gartnerpeerinsights #aiadoption #ai4good #llmops #openai #microsoftai #googleai #aicommunity #llms #gpt4 #newrelic #dubaistartups
Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads
arxiv.org
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pub.towardsai.net: In part 2 of "Unlocking Document Intelligence: E2E Azure-Powered Chatbot with Vector-Based Search," the focus is on querying the vector store using natural language questions. The article discusses the architecture and implementation of the chat pipeline, which integrates Azure Cognitive Search for document retrieval and OpenAI’s GPT-3.5 Turbo for generating responses. The Python code for the chat implementation is also outlined, providing a comprehensive understanding of the document processing pipeline. The content emphasizes the applicability of this pipeline for various domains and the importance of understanding its components for customization.
Unlocking Document Intelligence: E2E Azure-Powered Chatbot with Vector-Based Search (Part 2 — Q&A)
pub.towardsai.net
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The attention mechanism has given considerable impetus to the development of new technologies for the analysis of text and images. Nowadays the transformers type architecture is used from large language model such as BERT, GPT or Llama to Vision Transformers. What happens if these technologies are coupled together? New branches of research were born from the interactions between vision and language, such as visual question answering, image captioning, image-text retrieval as well as generate new images from text prompt. I published this story in Medium. I will tell you how the models BLIP and BLIP-2 work to solve the image2image and text2image tasks by a multimodal approach. As an Italian, I couldn't help but test these architectures in a small toy dataset of rich and tasty dishes. Enjoy your meal! #ArtificialIntelligence #LLM #Vision #Language #machinelearning https://lnkd.in/dQHfdUBX
Image and text features extraction with BLIP and BLIP-2: how to build a multimodal search engine
medium.com
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The internals of a large language model are wired up so that the next suggested word will be a natural continuation of the prompt, complete with its grammar, semantics and sentiment. Equipping a function with such a logic became possible through a series of scientific breakthroughs (and programming drudgery) that have resulted in the development of the family of algorithms known as GPT, or Generative Pre-trained Transformer #genai #dataanalytics #governai
Happy New Year: GPT in 500 lines of SQL - EXPLAIN EXTENDED
https://explainextended.com
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Open Language Model (OLMo): 7B parameter model. Includes open training/data, full model weights, evaluation, and fine-tuning code. Impressive performance on various generative tasks. There is also a. smaller OLMo 1B. They trained on both 1,024 @AMD MI250X and 216 Nvidia A100: "both runs resulted in nearly identical performance". Increasingly diverse hardware choices in AI infra.
Today, we’re releasing our first pretrained Open Language Models (OLMo) at the Allen Institute for AI (AI2), a set of 7 billion parameter models and one 1 billion parameter variant. This line of work was probably the main reason I joined AI2 and is the biggest lever I see possible to enact meaningful change in how AI is used, studied, and discussed in the short term. OLMo will represent a new type of LLM enabling new approaches to ML research and deployment, because on a key axis of openness, OLMo represents something entirely different. OLMo is built for scientists to be able to develop research directions at every point in the development process and execute on them, which was previously not available due to incomplete information and tools. Depending on the evaluation methods, OLMo 1 is either the best 7 billion parameter base model available for download or one of the best. This relies on a new way of thinking where models are judged on parameter plus token budget, similar to how scaling laws are measured for LLMs. You can find the core model here: https://lnkd.in/gxkCzPnU I wrote about it here (personal take): https://lnkd.in/ga5FQgu7 The technical paper is here: https://lnkd.in/gTMgiV-T
allenai/OLMo-7B · Hugging Face
huggingface.co
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This will fully change the landscape of pretrained language models
Today, we’re releasing our first pretrained Open Language Models (OLMo) at the Allen Institute for AI (AI2), a set of 7 billion parameter models and one 1 billion parameter variant. This line of work was probably the main reason I joined AI2 and is the biggest lever I see possible to enact meaningful change in how AI is used, studied, and discussed in the short term. OLMo will represent a new type of LLM enabling new approaches to ML research and deployment, because on a key axis of openness, OLMo represents something entirely different. OLMo is built for scientists to be able to develop research directions at every point in the development process and execute on them, which was previously not available due to incomplete information and tools. Depending on the evaluation methods, OLMo 1 is either the best 7 billion parameter base model available for download or one of the best. This relies on a new way of thinking where models are judged on parameter plus token budget, similar to how scaling laws are measured for LLMs. You can find the core model here: https://lnkd.in/gxkCzPnU I wrote about it here (personal take): https://lnkd.in/ga5FQgu7 The technical paper is here: https://lnkd.in/gTMgiV-T
allenai/OLMo-7B · Hugging Face
huggingface.co
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“OLMo represents the first time in a while (maybe before GPT2) that a state-of-the-art language model is fully transparent and open.” — Nathan Lambert “Depending on the evaluation methods, OLMo 1 [released by the Allen Institute for AI (AI2)] is either the best 7 billion parameter base model available for download or one of the best.” “Key points: Evaluation: OLMo is strong on a bunch of classic generation benchmarks, but lags slightly on tasks like MMLU and GSM8k. We have a lot of experiments to run on instruction-tuning, where those popular evaluations actually matter more. Per-token capabilities: the right way to look at models in 2024 is per-token training efficiency. OLMo edges out Llama 2 by training on about 20% more tokens (2.5T vs 2T). It’s rumored that Mistral 7b is trained on 2-4x as many tokens as Llama 2, so we don’t compare too much to it. Pythia is trained on <50% of the tokens of Llama and OLMo. Open training data: The exact dataset and tools for curating it are released under the Dolma project. License: Models and code are released under Apache 2.0, with the dataset under the AI2 ImpACT license. This is close to an “open-source” ML model, but that’s an ongoing debate. Artifacts: Collection on HuggingFace with links to models and dataset (Dolma). We release 4 7B models with different end of training annealing, hardwares (AMD and Nvidia), and final token counts from the same initialization. Paper: The paper is detailed and has lots of lessons on pretraining and base model evaluation (the Arxiv version coming soon). A technical blog post and press release are available separately. Communications: A technical blog post and press release are available. Plus, plenty of popular news outlets are covering it. Code: Training code, eval code, and fine-tuning code are all available. Lots more coming soon: AI2 plans on releasing bigger models, fine-tuned models, demos, analysis tools, evaluations, and more this year.” #technology #ai #innovation
Today, we’re releasing our first pretrained Open Language Models (OLMo) at the Allen Institute for AI (AI2), a set of 7 billion parameter models and one 1 billion parameter variant. This line of work was probably the main reason I joined AI2 and is the biggest lever I see possible to enact meaningful change in how AI is used, studied, and discussed in the short term. OLMo will represent a new type of LLM enabling new approaches to ML research and deployment, because on a key axis of openness, OLMo represents something entirely different. OLMo is built for scientists to be able to develop research directions at every point in the development process and execute on them, which was previously not available due to incomplete information and tools. Depending on the evaluation methods, OLMo 1 is either the best 7 billion parameter base model available for download or one of the best. This relies on a new way of thinking where models are judged on parameter plus token budget, similar to how scaling laws are measured for LLMs. You can find the core model here: https://lnkd.in/gxkCzPnU I wrote about it here (personal take): https://lnkd.in/ga5FQgu7 The technical paper is here: https://lnkd.in/gTMgiV-T
allenai/OLMo-7B · Hugging Face
huggingface.co
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AI Engineer @ LinkedIn | Posts weekly about AI
2moSounds very cool! Will be reading this one.