# text-generation

A language-model task that generates continuations from a supplied text prompt.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Trajectory as the Teacher: Few-Step Discrete Flow Matching via Energy-Navigated Distillation

DevFeed: [Trajectory as the Teacher: Few-Step Discrete Flow Matching via Energy-Navigated Distillation](<https://devfeed.tech/articles/trajectory-as-the-teacher-few-step-discrete-flow-matching-via-energy-navigated-distillation-31491.md>)

Original publisher: [Read original article](<https://machinelearning.apple.com/research/trajectory-teacher-flow-matching>)

Published: 2026-09-16T00:00:00Z

Content type: article

Language: en

Sources: [Apple Machine Learning Research](<https://devfeed.tech/sources/apple-machine-learning-research.md>)

Topics: [text-generation](<https://devfeed.tech/topics/text-generation.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [Inference](<https://devfeed.tech/topics/inference.md>)

Tags: [inference](<https://devfeed.tech/tags/inference.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [performance](<https://devfeed.tech/tags/performance.md>), [perplexity](<https://devfeed.tech/tags/perplexity.md>), [research](<https://devfeed.tech/tags/research.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>), [tokens](<https://devfeed.tech/tags/tokens.md>)

### AI overview

The article introduces Trajectory-Shaped Discrete Flow Matching, a training method that guides intermediate trajectory decisions with an energy-based coherence measure. The authors argue that poor distillation trajectories, rather than insufficient student capacity, limit few-step generation. On a 170M-parameter language-modeling task, an 8-step student reportedly achieves lower perplexity than a 1,024-step teacher while reducing inference steps.

### Source excerpt

Discrete flow matching generates text by iteratively transforming noise tokens into coherent language, but may require hundreds of forward passes. Distillation uses the multi-step trajectory to train a student to reproduce the process in a few steps. When the student underperforms, the usual explanation is insufficient capacity. We argue the opposite: the trajectory is the bottleneck, not the student. Each training trajectory is built through a chain of blind stochastic jumps with no evaluation of sequence quality; a single bad decision at an early midpoint propagates through subsequent steps...

## How Speculative Decoding Can Make LLM Generation 2-3 Times Faster

DevFeed: [How Speculative Decoding Can Make LLM Generation 2-3 Times Faster](<https://devfeed.tech/articles/how-to-make-llms-3x-faster-17992.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/how-to-make-llms-3x-faster>)

Author: ByteByteGo

Published: 2026-08-26T15:30:34Z

Content type: tutorial

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>), [GPU](<https://devfeed.tech/topics/gpu.md>)

Tags: [article](<https://devfeed.tech/tags/article.md>), [data-center](<https://devfeed.tech/tags/data-center.md>), [decoding](<https://devfeed.tech/tags/decoding.md>), [generation](<https://devfeed.tech/tags/generation.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [llms](<https://devfeed.tech/tags/llms.md>), [tokens](<https://devfeed.tech/tags/tokens.md>)

### AI overview

This tutorial explains speculative decoding, in which a smaller model proposes candidate tokens and a larger model evaluates them in a single forward pass. It covers autoregressive generation, GPU utilization, candidate acceptance and rejection, output-quality preservation, acceptance rates, draft sources, and when the technique may stop helping.

### Source excerpt

In this article, we will look at how speculative decoding works.

## How Fyxer built an AI executive assistant people trust

DevFeed: [How Fyxer built an AI executive assistant people trust](<https://devfeed.tech/articles/how-fyxer-built-an-ai-executive-assistant-people-trust-17415.md>)

Original publisher: [Read original article](<https://openai.com/index/fyxer>)

Published: 2026-08-13T12:00:00Z

Content type: article

Language: en

Sources: [OpenAI News](<https://devfeed.tech/sources/openai-news.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [OpenAI](<https://devfeed.tech/topics/openai.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [email](<https://devfeed.tech/tags/email.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [memory](<https://devfeed.tech/tags/memory.md>), [models](<https://devfeed.tech/tags/models.md>), [openai](<https://devfeed.tech/tags/openai.md>), [predictions](<https://devfeed.tech/tags/predictions.md>), [startup](<https://devfeed.tech/tags/startup.md>), [workflow](<https://devfeed.tech/tags/workflow.md>)

### AI overview

Fyxer built an AI executive assistant that uses OpenAI models, fine-tuning, memory, and user feedback to organize inboxes and draft emails in each user's voice. Its system divides email tasks among dozens of specialized models that use context to predict whether a reply is needed and generate suitable responses.

### Source excerpt

Fyxer uses OpenAI models, fine-tuning, memory, and real user feedback to organize inboxes and draft emails in each user's voice.

## 10 LLM Inference Optimization Techniques, Simply Explained

DevFeed: [10 LLM Inference Optimization Techniques, Simply Explained](<https://devfeed.tech/articles/10-llm-inference-optimization-techniques-simply-explained-18352.md>)

Original publisher: [Read original article](<https://levelup.gitconnected.com/10-llm-inference-optimization-techniques-simply-explained-99f79a12d084?source=rss-f10e9a50984a------2>)

Author: Dr. Ashish Bamania

Published: 2026-08-07T15:39:59Z

Content type: tutorial

Language: en

Sources: [Dr. Ashish Bamania](<https://devfeed.tech/sources/dr-ashish-bamania.md>)

Topics: [Inference](<https://devfeed.tech/topics/inference.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Caching](<https://devfeed.tech/topics/caching.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [caching](<https://devfeed.tech/tags/caching.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llm](<https://devfeed.tech/tags/llm.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [programming](<https://devfeed.tech/tags/programming.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [techniques](<https://devfeed.tech/tags/techniques.md>), [technology](<https://devfeed.tech/tags/technology.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>), [token](<https://devfeed.tech/tags/token.md>), [tokens](<https://devfeed.tech/tags/tokens.md>)

### AI overview

This tutorial explains ten techniques for making large language model inference faster and cheaper, including KV caching, quantization, FlashAttention, and PagedAttention. The supplied excerpt begins by describing how KV caching reduces repeated attention work during autoregressive text generation.

### Source excerpt

10 techniques that make LLM inference faster and cheaper: KV caching, Quantization, FlashAttention, PagedAttention, and more. Continue reading on Level Up Coding "

## 10 LLM Inference Optimization Techniques, Simply Explained

DevFeed: [10 LLM Inference Optimization Techniques, Simply Explained](<https://devfeed.tech/articles/10-llm-inference-optimization-techniques-simply-explained-18270.md>)

Original publisher: [Read original article](<https://www.intoai.pub/p/10-llm-inference-optimization-techniques>)

Author: Dr. Ashish Bamania

Published: 2026-08-01T11:15:38Z

Content type: tutorial

Language: en

Sources: [Into AI](<https://devfeed.tech/sources/into-ai.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Caching](<https://devfeed.tech/topics/caching.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>), [quantization](<https://devfeed.tech/topics/quantization.md>), [GPU](<https://devfeed.tech/topics/gpu.md>)

Tags: [caching](<https://devfeed.tech/tags/caching.md>), [decoding](<https://devfeed.tech/tags/decoding.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llms](<https://devfeed.tech/tags/llms.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>)

### AI overview

A visual guide to ten techniques for optimizing LLM inference, including KV caching, quantization, speculative decoding, and prefill-decode disaggregation. It explains how KV caching reduces repeated computation while increasing HBM memory usage.

### Source excerpt

A visual guide to 10 techniques, from KV caching and Quantization to Speculative decoding and Prefill-decode disaggregation, that make LLM inference faster and cheaper.

## DeepInfra on Hugging Face Inference Providers 🔥

DevFeed: [DeepInfra on Hugging Face Inference Providers 🔥](<https://devfeed.tech/articles/deepinfra-on-hugging-face-inference-providers-7279.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/inference-providers-deepinfra>)

Author: Aray Sultanbekova; Shang-Pin; Utemuratov; Yessen K; Oguz Vuruskaner; Célina Hanouti; Simon Brandeis; Lucain Pouget

Published: 2026-04-29T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [deepinfra](<https://devfeed.tech/topics/deepinfra.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [inference-providers](<https://devfeed.tech/topics/inference-providers.md>), [AI Inference](<https://devfeed.tech/topics/ai-inference.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [API keys](<https://devfeed.tech/topics/api-keys.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>), [text-to-image](<https://devfeed.tech/topics/text-to-image.md>)

Tags: [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [api](<https://devfeed.tech/tags/api.md>), [api-keys](<https://devfeed.tech/tags/api-keys.md>), [applications](<https://devfeed.tech/tags/applications.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [code](<https://devfeed.tech/tags/code.md>), [deepinfra](<https://devfeed.tech/tags/deepinfra.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [hub](<https://devfeed.tech/tags/hub.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-providers](<https://devfeed.tech/tags/inference-providers.md>), [javascript](<https://devfeed.tech/tags/javascript.md>), [llms](<https://devfeed.tech/tags/llms.md>), [open](<https://devfeed.tech/tags/open.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [partnerships](<https://devfeed.tech/tags/partnerships.md>), [pricing](<https://devfeed.tech/tags/pricing.md>), [python](<https://devfeed.tech/tags/python.md>), [sdks](<https://devfeed.tech/tags/sdks.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>), [text-to-image](<https://devfeed.tech/tags/text-to-image.md>)

### AI overview

DeepInfra is now a supported Inference Provider on the Hugging Face Hub, with serverless access to more than 100 models and integration with Hugging Face's JavaScript and Python SDKs. The article describes provider selection, API-key and routed-by-Hugging-Face modes, supported model tasks, and initial access to conversational and text-generation models.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Diffusion LLMs, Explained Simply

DevFeed: [Diffusion LLMs, Explained Simply](<https://devfeed.tech/articles/diffusion-llms-explained-simply-18354.md>)

Original publisher: [Read original article](<https://levelup.gitconnected.com/diffusion-llms-explained-simply-4dba963911c3?source=rss-f10e9a50984a------2>)

Author: Dr. Ashish Bamania

Published: 2026-04-24T14:23:01Z

Content type: tutorial

Language: en

Sources: [Dr. Ashish Bamania](<https://devfeed.tech/sources/dr-ashish-bamania.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>), [Transformer](<https://devfeed.tech/topics/transformer.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [introduction](<https://devfeed.tech/tags/introduction.md>), [llms](<https://devfeed.tech/tags/llms.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [programming](<https://devfeed.tech/tags/programming.md>), [technology](<https://devfeed.tech/tags/technology.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>), [tokens](<https://devfeed.tech/tags/tokens.md>)

### AI overview

An introductory explanation of diffusion large language models, contrasting them with autoregressive Transformer-based chatbots that generate responses sequentially, one token at a time.

### Source excerpt

A gentle but comprehensive introduction to Diffusion LLMs. Continue reading on Level Up Coding "

## Laravel March Product Updates

DevFeed: [Laravel March Product Updates](<https://devfeed.tech/articles/laravel-march-product-updates-3780.md>)

Original publisher: [Read original article](<https://laravel.com/blog/laravel-march-product-updates>)

Author: Laravel Team

Published: 2026-04-01T22:26:49Z

Content type: news

Language: en

Sources: [Laravel Blog](<https://devfeed.tech/sources/laravel-blog.md>)

Topics: [Laravel](<https://devfeed.tech/topics/laravel.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [PHP](<https://devfeed.tech/topics/php.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [AI search](<https://devfeed.tech/topics/ai-search.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>), [JSON:API](<https://devfeed.tech/topics/json-api.md>), [PostgreSQL](<https://devfeed.tech/topics/postgresql.md>), [Server-side rendering](<https://devfeed.tech/topics/server-side-rendering.md>), [Vite](<https://devfeed.tech/topics/vite.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [api](<https://devfeed.tech/tags/api.md>), [apps](<https://devfeed.tech/tags/apps.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [development](<https://devfeed.tech/tags/development.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [github](<https://devfeed.tech/tags/github.md>), [laravel](<https://devfeed.tech/tags/laravel.md>), [observability](<https://devfeed.tech/tags/observability.md>), [php](<https://devfeed.tech/tags/php.md>), [postgresql](<https://devfeed.tech/tags/postgresql.md>), [product](<https://devfeed.tech/tags/product.md>), [search](<https://devfeed.tech/tags/search.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>), [updates](<https://devfeed.tech/tags/updates.md>)

### AI overview

Laravel's March product updates introduce Laravel 13 with first-party AI tooling, including a unified SDK for text generation, agents, embeddings, audio, semantic search, and JSON:API resources. Laravel Cloud adds trial credits, GitHub SSO, scheduled autoscaling, and a redesigned usage page, while Forge adds managed PostgreSQL databases with observability, read replicas, and automated backups.

### Source excerpt

# Laravel March Product Updates March was a big month for product updates. Laravel 13 brings first-party AI tooling to the framework, Laravel Cloud now has a free trial with no credit card required,...

## Falcon Perception

DevFeed: [Falcon Perception](<https://devfeed.tech/articles/falcon-perception-7510.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/tiiuae/falcon-perception>)

Author: Basma Boussaha; FalconPerception

Published: 2026-04-01T07:13:20Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Transformer](<https://devfeed.tech/topics/transformer.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>), [long-context](<https://devfeed.tech/topics/long-context.md>)

Tags: [benchmark](<https://devfeed.tech/tags/benchmark.md>), [blog](<https://devfeed.tech/tags/blog.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [model](<https://devfeed.tech/tags/model.md>), [ocr](<https://devfeed.tech/tags/ocr.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [performance](<https://devfeed.tech/tags/performance.md>), [scale](<https://devfeed.tech/tags/scale.md>), [technology](<https://devfeed.tech/tags/technology.md>), [training](<https://devfeed.tech/tags/training.md>), [vision](<https://devfeed.tech/tags/vision.md>)

### AI overview

Falcon Perception is a 0.6B-parameter early-fusion Transformer for open-vocabulary grounding and segmentation from natural-language prompts. The article also introduces PBench, a diagnostic benchmark for perception capabilities and crowded long-context scenes, and Falcon OCR, a 0.3B-parameter open-source OCR model.

### Source excerpt

A Blog post by Technology Innovation Institute on Hugging Face

## Gemini and Angular, Part II: Creating Generative UIs

DevFeed: [Gemini and Angular, Part II: Creating Generative UIs](<https://devfeed.tech/articles/gemini-and-angular-part-ii-creating-generative-uis-37466.md>)

Original publisher: [Read original article](<https://www.angularspace.com/gemini-and-angular-part-ii-creating-generative-uis/>)

Author: Armen Vardanyan

Published: 2026-01-22T13:48:08Z

Content type: tutorial

Language: en

Sources: [Daniel Glejzner](<https://devfeed.tech/sources/daniel-glejzner.md>)

Topics: [Angular](<https://devfeed.tech/topics/angular.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [function calling](<https://devfeed.tech/topics/function-calling.md>), [ui](<https://devfeed.tech/topics/ui.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>), [JSON](<https://devfeed.tech/topics/json.md>), [App](<https://devfeed.tech/topics/app.md>)

Tags: [angular](<https://devfeed.tech/tags/angular.md>), [article](<https://devfeed.tech/tags/article.md>), [code](<https://devfeed.tech/tags/code.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [gemini](<https://devfeed.tech/tags/gemini.md>), [generative](<https://devfeed.tech/tags/generative.md>), [generative-ui](<https://devfeed.tech/tags/generative-ui.md>), [json](<https://devfeed.tech/tags/json.md>), [llms](<https://devfeed.tech/tags/llms.md>), [model](<https://devfeed.tech/tags/model.md>), [schemas](<https://devfeed.tech/tags/schemas.md>), [structured](<https://devfeed.tech/tags/structured.md>), [text](<https://devfeed.tech/tags/text.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>)

### AI overview

This tutorial continues a series on LLMs and Gemini by exploring Generative UI in Angular. It describes dynamically rendering interactive Angular components from model responses, combining Gemini with Google's Nano Banana Pro image generation model, and using Angular signal forms. The article emphasizes minimizing boilerplate while balancing model capability, performance, and cost.

### Source excerpt

Let's continue our journey into LLMs and Gemini! In the previous article, we moved beyond simple text generation and learned: how to force the model to speak our language using structured outputs (JSON schemas) how to connect the model to our actual code and logic using function calling

## Figma's 2025 AI report: Perspectives from designers and developers

DevFeed: [Figma's 2025 AI report: Perspectives from designers and developers](<https://devfeed.tech/articles/figma-s-2025-ai-report-perspectives-from-designers-and-developers-9660.md>)

Original publisher: [Read original article](<https://www.figma.com/blog/figma-2025-ai-report-perspectives/>)

Author: Andrew Hogan

Published: 2025-04-24T00:00:00Z

Content type: article

Language: en

Sources: [Figma Blog](<https://devfeed.tech/sources/figma-blog.md>)

Topics: [Figma](<https://devfeed.tech/topics/figma.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>)

Tags: [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [developers](<https://devfeed.tech/tags/developers.md>), [figma](<https://devfeed.tech/tags/figma.md>), [report](<https://devfeed.tech/tags/report.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>)

### AI overview

Figma's 2025 AI report surveys 2,500 Figma users about how designers and developers are building AI products. It finds that text generation is the most commonly shipped product type, while agentic AI is the fastest-growing category and requires careful design, testing, and prototyping decisions.

### Source excerpt

Figma's AI report tells us how designers and developers are navigating the changing landscape.

## Mastering Long Contexts in LLMs with KVPress

DevFeed: [Mastering Long Contexts in LLMs with KVPress](<https://devfeed.tech/articles/mastering-long-contexts-in-llms-with-kvpress-7383.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/nvidia/kvpress>)

Author: Simon Jegou; Maximilian Jeblick

Published: 2025-01-23T08:03:03Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Compression](<https://devfeed.tech/topics/compression.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [Cache](<https://devfeed.tech/topics/cache.md>), [LLM Techniques](<https://devfeed.tech/topics/llm-techniques.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>), [model architecture](<https://devfeed.tech/topics/model-architecture.md>)

Tags: [architecture](<https://devfeed.tech/tags/architecture.md>), [cache](<https://devfeed.tech/tags/cache.md>), [compression](<https://devfeed.tech/tags/compression.md>), [context-window](<https://devfeed.tech/tags/context-window.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [llms](<https://devfeed.tech/tags/llms.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [model](<https://devfeed.tech/tags/model.md>), [model-architecture](<https://devfeed.tech/tags/model-architecture.md>), [models](<https://devfeed.tech/tags/models.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>), [tokens](<https://devfeed.tech/tags/tokens.md>)

### AI overview

This article introduces KVPress, an NVIDIA toolkit that applies KV cache compression techniques to make long-context Large Language Models (LLMs) more memory-efficient. It explains how context windows enable in-context retrieval, learning, and extended reasoning, and why KV Cache memory usage grows with context length. The article also describes how KV Cache reuses attention-layer keys and values during autoregressive text generation.

### Source excerpt

TL;DR: KVPress packs the latest KV cache compression techniques, enabling memory-efficient long-context LLMs. 🚀 One of the key features of Large Language Models (LLMs) is their context window--the maximum number of tokens they can process in a single request. As LLMs evolve, their context windows are becoming increasingly larger. Larger context windows unlock incredible possibilities: - In-context retrieval: Seamlessly referencing large amounts of text within a single query.

## Benchmarking Language Model Performance on 5th Gen Xeon at GCP

DevFeed: [Benchmarking Language Model Performance on 5th Gen Xeon at GCP](<https://devfeed.tech/articles/benchmarking-language-model-performance-on-5th-gen-xeon-at-gcp-7290.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/intel-gcp-c4>)

Author: Matrix Yao; Ke Ding; Ilyas Moutawwakil

Published: 2024-12-17T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [benchmarking](<https://devfeed.tech/topics/benchmarking.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [cpu](<https://devfeed.tech/topics/cpu.md>), [Google Cloud Platform (GCP)](<https://devfeed.tech/topics/google-cloud.md>), [intel](<https://devfeed.tech/topics/intel.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [llama3](<https://devfeed.tech/topics/llama3.md>)

Tags: [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [architectures](<https://devfeed.tech/tags/architectures.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [backend](<https://devfeed.tech/tags/backend.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [benchmarking](<https://devfeed.tech/tags/benchmarking.md>), [compute](<https://devfeed.tech/tags/compute.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [gcp](<https://devfeed.tech/tags/gcp.md>), [google](<https://devfeed.tech/tags/google.md>), [google-cloud](<https://devfeed.tech/tags/google-cloud.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [intel](<https://devfeed.tech/tags/intel.md>), [llama3](<https://devfeed.tech/tags/llama3.md>), [llm](<https://devfeed.tech/tags/llm.md>), [performance](<https://devfeed.tech/tags/performance.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>)

### AI overview

This article benchmarks language-model performance on Google Cloud Compute Engine C4 and N2 instances powered by different generations of Intel Xeon processors. It compares text embedding and text generation for agentic AI workloads, focusing on the benefits of Intel Advanced Matrix Extensions (AMX) and CPU-based hosting of systems using small language models such as Meta's 1B and 3B Llama 3.2 models.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Faster Text Generation with Self-Speculative Decoding

DevFeed: [Faster Text Generation with Self-Speculative Decoding](<https://devfeed.tech/articles/faster-text-generation-with-self-speculative-decoding-7307.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/layerskip>)

Author: Aritra Roy Gosthipaty; Mostafa Elhoushi; Pedro Cuenca; Vaibhav Srivastav

Published: 2024-11-20T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [text-generation](<https://devfeed.tech/topics/text-generation.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Transformers](<https://devfeed.tech/topics/transformers.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>)

Tags: [benchmark](<https://devfeed.tech/tags/benchmark.md>), [collaboration](<https://devfeed.tech/tags/collaboration.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [inference](<https://devfeed.tech/tags/inference.md>), [latency](<https://devfeed.tech/tags/latency.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [research](<https://devfeed.tech/tags/research.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>), [transformers](<https://devfeed.tech/tags/transformers.md>)

### AI overview

This article explains self-speculative decoding, a technique that uses the early layers of a large language model to generate draft tokens and its deeper layers to verify them. With specialized training, the approach can accelerate text generation, reduce computational costs and latency, save memory, and enable inference on smaller GPUs. The article also demonstrates the method with the transformers library, code examples, benchmarks, and practical deployment guidance.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Introduction to Cloud-based AI

DevFeed: [Introduction to Cloud-based AI](<https://devfeed.tech/articles/introduction-to-cloud-based-ai-11512.md>)

Original publisher: [Read original article](<https://www.kodeco.com/ai/paths/cloud-based-ai>)

Published: 2024-11-14T00:00:00Z

Content type: tutorial

Language: en

Sources: [Kodeco | High quality programming tutorials: iOS, Android, Swift, Kotlin, Unity, and more](<https://devfeed.tech/sources/kodeco-high-quality-programming-tutorials-ios-android-swift-kotlin-unity-and-more.md>)

Topics: [Cloud](<https://devfeed.tech/topics/cloud.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [AI Development](<https://devfeed.tech/topics/ai-development.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>), [Image processing](<https://devfeed.tech/topics/image-processing.md>), [Google](<https://devfeed.tech/topics/google.md>), [OpenAI](<https://devfeed.tech/topics/openai.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [apis](<https://devfeed.tech/tags/apis.md>), [audio](<https://devfeed.tech/tags/audio.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [course](<https://devfeed.tech/tags/course.md>), [development](<https://devfeed.tech/tags/development.md>), [google](<https://devfeed.tech/tags/google.md>), [image-processing](<https://devfeed.tech/tags/image-processing.md>), [openai](<https://devfeed.tech/tags/openai.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>)

### AI overview

This learning path introduces cloud-based AI services for text generation, image processing, and audio analysis. It provides hands-on experience with OpenAI and Google Gemini and covers building AI apps with cloud services and APIs.

### Source excerpt

This course introduces you to cloud-based AI services, focusing on text generation, image processing, and audio analysis. You'll gain hands-on experience with leading platforms such as OpenAI and Google Gemini, learning to leverage these technologies for various AI apps.

## Universal Assisted Generation: Faster Decoding with Any Assistant Model

DevFeed: [Universal Assisted Generation: Faster Decoding with Any Assistant Model](<https://devfeed.tech/articles/universal-assisted-generation-faster-decoding-with-any-assistant-model-7546.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/universal_assisted_generation>)

Author: Daniel Korat; Oren Pereg; Moshe Berchansky; Jonathan Mamou; Joao Gante; Lewis Tunstall; Nadav Timor; Moshe Wasserblat

Published: 2024-10-29T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Inference](<https://devfeed.tech/topics/inference.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>), [LLMs](<https://devfeed.tech/topics/llms.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [intel](<https://devfeed.tech/topics/intel.md>)

Tags: [collaboration](<https://devfeed.tech/tags/collaboration.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [inference](<https://devfeed.tech/tags/inference.md>), [intel](<https://devfeed.tech/tags/intel.md>), [latency](<https://devfeed.tech/tags/latency.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [research](<https://devfeed.tech/tags/research.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>)

### AI overview

This article explains Universal Assisted Generation, a technique that accelerates large language model text generation by using a smaller assistant model to propose tokens and a target model to verify them. The approach extends assisted generation to pairs of models with different tokenizers and can provide 1.5x-3x speedups depending on the task and models used.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Faster assisted generation support for Intel Gaudi

DevFeed: [Faster assisted generation support for Intel Gaudi](<https://devfeed.tech/articles/faster-assisted-generation-support-for-intel-gaudi-7108.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/assisted-generation-support-gaudi>)

Author: Haim Barad; Neha Raste; Tien Pei Chou

Published: 2024-06-04T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [intel](<https://devfeed.tech/topics/intel.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Cache](<https://devfeed.tech/topics/cache.md>), [Transformer](<https://devfeed.tech/topics/transformer.md>), [optimum](<https://devfeed.tech/topics/optimum.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>)

Tags: [accelerate](<https://devfeed.tech/tags/accelerate.md>), [ai](<https://devfeed.tech/tags/ai.md>), [diffusers](<https://devfeed.tech/tags/diffusers.md>), [gaudi](<https://devfeed.tech/tags/gaudi.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [intel](<https://devfeed.tech/tags/intel.md>), [latency](<https://devfeed.tech/tags/latency.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [optimum](<https://devfeed.tech/tags/optimum.md>), [partnerships](<https://devfeed.tech/tags/partnerships.md>), [techniques](<https://devfeed.tech/tags/techniques.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>), [transformers](<https://devfeed.tech/tags/transformers.md>)

### AI overview

The article explains how assisted decoding and speculative sampling were adapted and optimized for Intel Gaudi processors. Integrated into Optimum Habana, these techniques use draft and target models, KV caching, and quantized models to accelerate text generation while preserving the target model's sampling quality.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Hugging Face on AMD Instinct MI300 GPU

DevFeed: [Hugging Face on AMD Instinct MI300 GPU](<https://devfeed.tech/articles/hugging-face-on-amd-instinct-mi300-gpu-7245.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/huggingface-amd-mi300>)

Author: Félix Marty; Mohit Sharma; seungrok jung; Morgan Funtowicz

Published: 2024-05-21T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [AI Models](<https://devfeed.tech/topics/ai-models.md>), [CI/CD](<https://devfeed.tech/topics/cicd.md>), [tgi](<https://devfeed.tech/topics/tgi.md>), [Azure](<https://devfeed.tech/topics/azure.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>), [migration](<https://devfeed.tech/topics/migration.md>)

Tags: [ai-models](<https://devfeed.tech/tags/ai-models.md>), [amd](<https://devfeed.tech/tags/amd.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [azure](<https://devfeed.tech/tags/azure.md>), [ci-cd](<https://devfeed.tech/tags/ci-cd.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [inference](<https://devfeed.tech/tags/inference.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [instinct](<https://devfeed.tech/tags/instinct.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [llama](<https://devfeed.tech/tags/llama.md>), [llm](<https://devfeed.tech/tags/llm.md>), [migration](<https://devfeed.tech/tags/migration.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [optimum](<https://devfeed.tech/tags/optimum.md>), [performance](<https://devfeed.tech/tags/performance.md>), [rocm](<https://devfeed.tech/tags/rocm.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>), [transformers](<https://devfeed.tech/tags/transformers.md>)

### AI overview

Hugging Face and AMD describe first-class integration for AMD Instinct MI300 GPU servers across the Hugging Face Platform. The article covers deployment from local development to Azure ND MI300x V5 VMs, compatibility with existing libraries and products, and CI/CD testing on managed Kubernetes infrastructure.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Unlocking Longer Generation with Key-Value Cache Quantization

DevFeed: [Unlocking Longer Generation with Key-Value Cache Quantization](<https://devfeed.tech/articles/unlocking-longer-generation-with-key-value-cache-quantization-7305.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/kv-cache-quantization>)

Author: Raushan Turganbay

Published: 2024-05-16T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [quantization](<https://devfeed.tech/topics/quantization.md>), [LLMs](<https://devfeed.tech/topics/llms.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>), [Transformer architecture](<https://devfeed.tech/topics/transformer-architecture.md>), [Language models](<https://devfeed.tech/topics/language-models.md>)

Tags: [efficiency](<https://devfeed.tech/tags/efficiency.md>), [generation](<https://devfeed.tech/tags/generation.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llms](<https://devfeed.tech/tags/llms.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [memory](<https://devfeed.tech/tags/memory.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>), [transformer-architecture](<https://devfeed.tech/tags/transformer-architecture.md>)

### AI overview

The article explains how key-value cache quantization reduces memory usage during long-context text generation in large language models, while offering trade-offs between memory efficiency, generation speed, and output quality.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Mistral releases Mistral Large language model through la Plateforme and Azure

DevFeed: [Mistral releases Mistral Large language model through la Plateforme and Azure](<https://devfeed.tech/articles/au-large-7059.md>)

Original publisher: [Read original article](<https://mistral.ai/news/mistral-large/>)

Published: 2024-02-26T08:00:00Z

Content type: release

Language: en

Sources: [Mistral AI Blog](<https://devfeed.tech/sources/mistral-ai-blog.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>), [Azure](<https://devfeed.tech/topics/azure.md>), [API](<https://devfeed.tech/topics/api.md>), [Code](<https://devfeed.tech/topics/code.md>), [Code generation](<https://devfeed.tech/topics/code-generation.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>), [long-context](<https://devfeed.tech/topics/long-context.md>), [Microsoft](<https://devfeed.tech/topics/microsoft.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [api](<https://devfeed.tech/tags/api.md>), [azure](<https://devfeed.tech/tags/azure.md>), [code](<https://devfeed.tech/tags/code.md>), [code-generation](<https://devfeed.tech/tags/code-generation.md>), [context-window](<https://devfeed.tech/tags/context-window.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [llm](<https://devfeed.tech/tags/llm.md>), [microsoft](<https://devfeed.tech/tags/microsoft.md>), [models](<https://devfeed.tech/tags/models.md>)

### AI overview

Mistral announces Mistral Large, a language model for multilingual reasoning, text understanding, transformation, and code generation. The release describes a 32K-token context window, function calling, instruction-following, and availability through la Plateforme and Azure.

### Source excerpt

The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models.

## Expert-in-the-loop generative AI for text generation at Stitch Fix

DevFeed: [Expert-in-the-loop generative AI for text generation at Stitch Fix](<https://devfeed.tech/articles/a-new-era-of-creativity-expert-in-the-loop-generative-ai-at-stitch-fix-29343.md>)

Original publisher: [Read original article](<https://multithreaded.stitchfix.com/blog/2023/03/06/expert-in-the-loop-generative-ai-at-stitch-fix/>)

Published: 2023-03-06T09:00:00Z

Content type: article

Language: en

Sources: [Stitch Fix](<https://devfeed.tech/sources/stitch-fix.md>)

Topics: [Generative AI](<https://devfeed.tech/topics/generative-ai.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [data](<https://devfeed.tech/topics/data.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>)

Tags: [data](<https://devfeed.tech/tags/data.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [few-shot](<https://devfeed.tech/tags/few-shot.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>)

### AI overview

Stitch Fix describes using generative AI and large language models for text-generation tasks, including advertisement headlines and product descriptions, with human experts involved in the process.

### Source excerpt

Generative AI has been gaining attention and popularity in recent years. Made possible with advances in deep learning algorithms and trained with previously unimaginable amounts of data, generative AI has already contributed many real-world use cases, from creating realistic images with systems like DALL-E 2 and Midjourney, to generating human-like responses with ChatGPT. At Stitch Fix, we are constantly exploring innovative ways to utilize the latest advancements in AI and ML to enhance the experiences of our clients. In this blog post, we will delve into our approach to generative AI, with a special focus on our text generation use cases. By combining algo-generated text with a human expert-in-the-loop approach, we aim to streamline tasks such as crafting engaging advertisement headlines and producing high-fidelity product descriptions. Algo-generated Ad Headlines Generative AI in the text space is powered by large language models (LLMs) that are pre-trained on vast amounts of data (for example, GPT-3 is pre-trained on nearly the entire internet) and can understand and generate natural language. However, once pre-trained, it can generalize from very limited amounts of data and is capable of performing a wide variety of natural language tasks such as Q&A, translation, summarization, and text generation. This few-shot learning capability, which relies only on a few examples to make predictions, makes it especially well suited for tasks that require creativity and originality, such as crafting compelling ad headlines. Ad headlines are often the first interaction with potential clients, so it's crucial to make them engaging. Traditional marketing requires a copywriter to write new headlines for every new ad asset, which can be time consuming and costly, and may not always result in unique copy. Using generative AI, such as GPT-3, we can quickly generate a large number of headlines tailored to our brand tone and messaging. We achieve this by using a combination of late

## Friday Q&A 2018-04-27: Generating Text With Markov Chains in Swift

DevFeed: [Friday Q&A 2018-04-27: Generating Text With Markov Chains in Swift](<https://devfeed.tech/articles/friday-q-a-2018-04-27-generating-text-with-markov-chains-in-swift-30627.md>)

Original publisher: [Read original article](<http://www.mikeash.com/pyblog/friday-qa-2018-04-27-generating-text-with-markov-chains-in-swift.html>)

Author: Mike Ash

Published: 2018-04-28T01:27:00Z

Content type: tutorial

Language: en

Sources: [Mike Ash](<https://devfeed.tech/sources/mike-ash.md>)

Topics: [Swift](<https://devfeed.tech/topics/swift.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>)

Tags: [build](<https://devfeed.tech/tags/build.md>), [swift](<https://devfeed.tech/tags/swift.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>)

### AI overview

This tutorial explains Markov chains and uses Swift to build a text generator from the blog's contents. It describes probabilistic state transitions, data representation, and tradeoffs between simple and more efficient ways to choose transitions.

### Source excerpt

Markov chains make for a simple way to generate realistic looking but nonsensical text. Today, I'm going to use that technique to build a text generator based on this blog's contents, an idea suggested/inspired by reader Jordan Pittman. (Read More)