# text-generation

Published articles for text-generation.

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 to Get Started With Ollama

DevFeed: [How to Get Started With Ollama](<https://devfeed.tech/articles/how-to-get-started-with-ollama-4366.md>)

Original publisher: [Read original article](<https://realpython.com/courses/get-started-with-ollama/>)

Author: Real Python

Published: 2026-09-01T14:00:00Z

Content type: tutorial

Language: en

Sources: [Real Python](<https://devfeed.tech/sources/real-python.md>)

Topics: [Language models](<https://devfeed.tech/topics/language-models.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [apps](<https://devfeed.tech/tags/apps.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [large-language-models-llms](<https://devfeed.tech/tags/large-language-models-llms.md>), [learn](<https://devfeed.tech/tags/learn.md>), [local](<https://devfeed.tech/tags/local.md>), [models](<https://devfeed.tech/tags/models.md>), [offline](<https://devfeed.tech/tags/offline.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [privacy](<https://devfeed.tech/tags/privacy.md>), [python](<https://devfeed.tech/tags/python.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>)

### AI overview

A video course on using Ollama and its Python SDK to run local LLMs, generate text, and build offline-capable AI applications.

### Source excerpt

Learn how to install Ollama, pull local models, and connect them to your Python code using the chat and text generation interfaces.

## 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 "

## Baseten on Hugging Face Inference Providers 🔥

DevFeed: [Baseten on Hugging Face Inference Providers 🔥](<https://devfeed.tech/articles/baseten-on-hugging-face-inference-providers-7121.md>)

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

Author: Alex Ker; Roland Crosby; Sid Shanker; Johan; Célina Hanouti; Simon Brandeis; Lucain Pouget; merve

Published: 2026-08-06T00:00:00Z

Content type: news

Language: en

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

Topics: [AI Chat](<https://devfeed.tech/topics/ai-chat.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>)

Tags: [baseten](<https://devfeed.tech/tags/baseten.md>), [deepseek](<https://devfeed.tech/tags/deepseek.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>), [llms](<https://devfeed.tech/tags/llms.md>), [partnerships](<https://devfeed.tech/tags/partnerships.md>), [sdks](<https://devfeed.tech/tags/sdks.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>)

### AI overview

Baseten is now an Inference Provider on the Hugging Face Hub, offering conversational and text-generation access to supported open-weight LLMs. The integration is available through Hugging Face Python and JavaScript SDKs, with custom-key and Hugging Face-routed request modes.

### Source excerpt

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

## 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.

## DiffusionGemma: 4x faster text generation

DevFeed: [DiffusionGemma: 4x faster text generation](<https://devfeed.tech/articles/diffusiongemma-4x-faster-text-generation-6147.md>)

Original publisher: [Read original article](<https://deepmind.google/blog/diffusiongemma-4x-faster-text-generation/>)

Author: Brendan O'Donoghue

Published: 2026-06-10T16:24:11Z

Content type: release

Language: en

Sources: [Google DeepMind News](<https://devfeed.tech/sources/google-deepmind-news.md>)

Topics: [Language models](<https://devfeed.tech/topics/language-models.md>), [Local AI](<https://devfeed.tech/topics/local-ai.md>)

Tags: [diffusion](<https://devfeed.tech/tags/diffusion.md>), [gemma](<https://devfeed.tech/tags/gemma.md>), [inference](<https://devfeed.tech/tags/inference.md>), [latency](<https://devfeed.tech/tags/latency.md>), [local](<https://devfeed.tech/tags/local.md>), [mixture-of-experts-moe](<https://devfeed.tech/tags/mixture-of-experts-moe.md>), [none](<https://devfeed.tech/tags/none.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>)

### AI overview

DiffusionGemma is an experimental Apache 2.0-licensed 26B MoE text-diffusion model that generates text blocks in parallel for up to 4x faster GPU generation. It targets speed-critical local interactive workflows, while standard Gemma 4 remains recommended for maximum output quality.

### Source excerpt

An overview of DiffusionGemma, an exceptionally fast text generation model with up to 4x faster speeds.

## 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,...

## Laravel AI SDK, Boost, or MCP: Which Tool Do You Need?

DevFeed: [Laravel AI SDK, Boost, or MCP: Which Tool Do You Need?](<https://devfeed.tech/articles/laravel-ai-sdk-boost-or-mcp-which-tool-do-you-need-3748.md>)

Original publisher: [Read original article](<https://laravel.com/blog/laravel-ai-sdk-boost-or-mcp-which-tool-do-you-need>)

Author: Ana Tavares

Published: 2026-03-04T17:18:51Z

Content type: article

Language: en

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

Topics: [Laravel](<https://devfeed.tech/topics/laravel.md>), [vercel ai sdk](<https://devfeed.tech/topics/vercel-ai-sdk.md>), [Laravel Boost](<https://devfeed.tech/topics/laravel-boost.md>), [MCP](<https://devfeed.tech/topics/mcp.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>), [API](<https://devfeed.tech/topics/api.md>), [text-generation](<https://devfeed.tech/topics/text-generation.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [api](<https://devfeed.tech/tags/api.md>), [laravel](<https://devfeed.tech/tags/laravel.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>), [tool](<https://devfeed.tech/tags/tool.md>), [writing-code](<https://devfeed.tech/tags/writing-code.md>)

### AI overview

This article compares Laravel's AI SDK, Laravel Boost, and Laravel MCP. It explains that the AI SDK adds AI capabilities to Laravel applications, Boost helps AI agents write Laravel code, and MCP lets external AI tools interact with an application.

### Source excerpt

Laravel AI SDK, Boost, and MCP serve different purposes. Learn when to use each one, how they work together, and how to build faster with Laravel.

## 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

## OVHcloud on Hugging Face Inference Providers 🔥

DevFeed: [OVHcloud on Hugging Face Inference Providers 🔥](<https://devfeed.tech/articles/ovhcloud-on-hugging-face-inference-providers-7027.md>)

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

Author: Gilles Closset; Fabien Ric; Elias Tourneux

Published: 2025-11-24T16:08:47Z

Content type: release

Language: en

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

Topics: [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [inference-providers](<https://devfeed.tech/topics/inference-providers.md>), [Serverless](<https://devfeed.tech/topics/serverless.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>), [AI Models](<https://devfeed.tech/topics/ai-models.md>), [API](<https://devfeed.tech/topics/api.md>)

Tags: [ai-models](<https://devfeed.tech/tags/ai-models.md>), [api](<https://devfeed.tech/tags/api.md>), [hub](<https://devfeed.tech/tags/hub.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [inference-providers](<https://devfeed.tech/tags/inference-providers.md>), [latency](<https://devfeed.tech/tags/latency.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [production](<https://devfeed.tech/tags/production.md>), [sdks](<https://devfeed.tech/tags/sdks.md>), [serverless](<https://devfeed.tech/tags/serverless.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>), [tokens](<https://devfeed.tech/tags/tokens.md>)

### AI overview

Hugging Face announces OVHcloud as a supported Inference Provider on the Hugging Face Hub. The serverless service provides managed access to open-weight and frontier AI models through API calls, with SDK integration, European infrastructure, pay-per-token pricing, low latency, multimodal capabilities, structured outputs, function calling, text generation, and embeddings.

### Source excerpt

We're thrilled to share that OVHcloud is now a supported Inference Provider on the Hugging Face Hub! OVHcloud joins our growing ecosystem, enhancing the breadth and capabilities of serverless inference directly on the Hub's model pages. Inference Providers are also seamlessly integrated into our client SDKs (for both JS and Python), making it super easy to use a wide variety of models with your preferred providers.

## Scaleway on Hugging Face Inference Providers 🔥

DevFeed: [Scaleway on Hugging Face Inference Providers 🔥](<https://devfeed.tech/articles/scaleway-on-hugging-face-inference-providers-7284.md>)

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

Author: Guillaume Noale; Franck Pagny; Fred Bardolle; Guillaume Calmettes; Constance Morales; Célina Hanouti; Julien Chaumond; Simon Brandeis; Lucain Pouget

Published: 2025-09-19T00:00:00Z

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>), [scaleway](<https://devfeed.tech/topics/scaleway.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [inference-providers](<https://devfeed.tech/topics/inference-providers.md>), [Serverless](<https://devfeed.tech/topics/serverless.md>), [gpt-oss](<https://devfeed.tech/topics/gpt-oss.md>), [SDKs](<https://devfeed.tech/topics/sdks.md>), [API keys](<https://devfeed.tech/topics/api-keys.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Sovereign AI](<https://devfeed.tech/topics/sovereign-ai.md>)

Tags: [api-keys](<https://devfeed.tech/tags/api-keys.md>), [enterprise](<https://devfeed.tech/tags/enterprise.md>), [function-calling](<https://devfeed.tech/tags/function-calling.md>), [gemma](<https://devfeed.tech/tags/gemma.md>), [gpt-oss](<https://devfeed.tech/tags/gpt-oss.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>), [llms](<https://devfeed.tech/tags/llms.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [open](<https://devfeed.tech/tags/open.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [partnerships](<https://devfeed.tech/tags/partnerships.md>), [providers](<https://devfeed.tech/tags/providers.md>), [qwen3](<https://devfeed.tech/tags/qwen3.md>), [scaleway](<https://devfeed.tech/tags/scaleway.md>), [sdks](<https://devfeed.tech/tags/sdks.md>), [serverless](<https://devfeed.tech/tags/serverless.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>)

### AI overview

Hugging Face announces Scaleway as a supported Inference Provider on the Hugging Face Hub. The integration provides serverless access to open-weight and frontier AI models through model pages, client SDKs, and APIs, with European data centers, pay-per-token pricing, low latency, and production-oriented features.

### Source excerpt

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

## Visible Watermarking with Gradio

DevFeed: [Visible Watermarking with Gradio](<https://devfeed.tech/articles/visible-watermarking-with-gradio-7563.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/watermarking-with-gradio>)

Author: Margaret Mitchell

Published: 2025-09-15T00:00:00Z

Content type: article

Language: en

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

Topics: [watermarking](<https://devfeed.tech/topics/watermarking.md>), [gradio](<https://devfeed.tech/topics/gradio.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Generative AI](<https://devfeed.tech/topics/generative-ai.md>)

Tags: [ai-content](<https://devfeed.tech/tags/ai-content.md>), [ai-transparency](<https://devfeed.tech/tags/ai-transparency.md>), [building](<https://devfeed.tech/tags/building.md>), [ethics](<https://devfeed.tech/tags/ethics.md>), [gradio](<https://devfeed.tech/tags/gradio.md>), [guide](<https://devfeed.tech/tags/guide.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>), [text-to-image](<https://devfeed.tech/tags/text-to-image.md>), [text-to-video](<https://devfeed.tech/tags/text-to-video.md>), [watermarking](<https://devfeed.tech/tags/watermarking.md>)

### AI overview

Hugging Face explains how Gradio can add visible watermarks to AI-generated images, video, and text in Spaces. The article highlights filename-based, open-image, NumPy-array, QR, custom text, and chatbot watermarking options to support attribution, transparency, and disclosure.

### Source excerpt

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

## Microsoft and Hugging Face expand collaboration

DevFeed: [Microsoft and Hugging Face expand collaboration](<https://devfeed.tech/articles/microsoft-and-hugging-face-expand-collaboration-7118.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/azure-ai-foundry>)

Author: Jeff Boudier; Simon Pagezy; Alvaro Bartolome

Published: 2025-05-19T00:00:00Z

Content type: news

Language: en

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

Topics: [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>), [vulnerability](<https://devfeed.tech/topics/vulnerability.md>)

Tags: [announcement](<https://devfeed.tech/tags/announcement.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [audio](<https://devfeed.tech/tags/audio.md>), [azure](<https://devfeed.tech/tags/azure.md>), [classification](<https://devfeed.tech/tags/classification.md>), [collaboration](<https://devfeed.tech/tags/collaboration.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [images](<https://devfeed.tech/tags/images.md>), [microsoft](<https://devfeed.tech/tags/microsoft.md>), [model](<https://devfeed.tech/tags/model.md>), [models](<https://devfeed.tech/tags/models.md>), [open](<https://devfeed.tech/tags/open.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [partnership](<https://devfeed.tech/tags/partnership.md>), [security](<https://devfeed.tech/tags/security.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>), [text-to-image](<https://devfeed.tech/tags/text-to-image.md>), [vulnerability](<https://devfeed.tech/tags/vulnerability.md>)

### AI overview

Microsoft and Hugging Face expanded their collaboration to make more than 10,000 Hugging Face models available through Azure AI Foundry. The collection supports deployment of models for text, audio, and image tasks, with listed security screening requirements.

### Source excerpt

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

## 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.

## Prefill and Decode for Concurrent Requests - Optimizing LLM Performance

DevFeed: [Prefill and Decode for Concurrent Requests - Optimizing LLM Performance](<https://devfeed.tech/articles/prefill-and-decode-for-concurrent-requests-optimizing-llm-performance-7517.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/tngtech/llm-performance-prefill-decode-concurrent-requests>)

Author: Benjamin Merkel

Published: 2025-04-16T10:10:58Z

Content type: article

Language: en

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

Topics: [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Language models](<https://devfeed.tech/topics/language-models.md>)

Tags: [blog](<https://devfeed.tech/tags/blog.md>), [cache](<https://devfeed.tech/tags/cache.md>), [hosting](<https://devfeed.tech/tags/hosting.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llm](<https://devfeed.tech/tags/llm.md>), [llms](<https://devfeed.tech/tags/llms.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [performance](<https://devfeed.tech/tags/performance.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>)

### AI overview

The article explains how LLM text generation differs between prefill, which processes prompt tokens to produce the first output token, and decode, which produces later tokens sequentially. It relates this distinction to KV caching and latency metrics such as time to first token and time per output token.

### Source excerpt

A Blog post by TNG Technology Consulting GmbH on Hugging Face

## A Deepdive into Aya Vision: Advancing the Frontier of Multilingual Multimodality

DevFeed: [A Deepdive into Aya Vision: Advancing the Frontier of Multilingual Multimodality](<https://devfeed.tech/articles/a-deepdive-into-aya-vision-advancing-the-frontier-of-multilingual-multimodality-7116.md>)

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

Author: Saurabh Dash; Yiyang Nan; Arash Ahmadian; John Dang

Published: 2025-03-04T00:00:00Z

Content type: article

Language: en

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

Topics: [aya](<https://devfeed.tech/topics/aya.md>), [vlm](<https://devfeed.tech/topics/vlm.md>), [cohere](<https://devfeed.tech/topics/cohere.md>), [Benchmark](<https://devfeed.tech/topics/benchmark.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>)

Tags: [aya](<https://devfeed.tech/tags/aya.md>), [benchmark](<https://devfeed.tech/tags/benchmark.md>), [cohere](<https://devfeed.tech/tags/cohere.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [llm](<https://devfeed.tech/tags/llm.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [performance](<https://devfeed.tech/tags/performance.md>), [synthetic](<https://devfeed.tech/tags/synthetic.md>), [technical](<https://devfeed.tech/tags/technical.md>), [techniques](<https://devfeed.tech/tags/techniques.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>), [vision](<https://devfeed.tech/tags/vision.md>), [vlm](<https://devfeed.tech/tags/vlm.md>)

### AI overview

Cohere For AI introduces Aya Vision, an open-weight multilingual and multimodal model family supporting language and vision understanding across 23 languages. The article describes its training techniques, benchmark results, open-weight releases, and image-processing architecture, including dynamic image tiling and latency improvements.

### Source excerpt

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

## 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.

## Local RAG with Ollama, Mistral, and Turso

DevFeed: [Local RAG with Ollama, Mistral, and Turso](<https://devfeed.tech/articles/local-rag-with-ollama-mistral-and-turso-5999.md>)

Original publisher: [Read original article](<https://turso.tech/blog/local-rag-with-ollama-and-turso-sqlite>)

Author: Jamie Barton

Published: 2025-01-08T00:00:00Z

Content type: tutorial

Language: en

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

Topics: [Retrieval-Augmented Generation](<https://devfeed.tech/topics/retrieval-augmented-generation.md>), [Ollama](<https://devfeed.tech/topics/ollama.md>), [Turso](<https://devfeed.tech/topics/turso.md>), [SQLite](<https://devfeed.tech/topics/sqlite.md>), [Embeddings](<https://devfeed.tech/topics/embeddings.md>), [libSQL](<https://devfeed.tech/topics/libsql.md>), [Local-First](<https://devfeed.tech/topics/local-first.md>), [Chat Bot](<https://devfeed.tech/topics/chatbot.md>), [pdf](<https://devfeed.tech/topics/pdf.md>)

Tags: [build](<https://devfeed.tech/tags/build.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [data](<https://devfeed.tech/tags/data.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [llm](<https://devfeed.tech/tags/llm.md>), [local](<https://devfeed.tech/tags/local.md>), [local-first](<https://devfeed.tech/tags/local-first.md>), [mistral](<https://devfeed.tech/tags/mistral.md>), [offline](<https://devfeed.tech/tags/offline.md>), [ollama](<https://devfeed.tech/tags/ollama.md>), [on-device](<https://devfeed.tech/tags/on-device.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [rag](<https://devfeed.tech/tags/rag.md>), [sql](<https://devfeed.tech/tags/sql.md>), [sqlite](<https://devfeed.tech/tags/sqlite.md>), [text-generation](<https://devfeed.tech/tags/text-generation.md>), [turso](<https://devfeed.tech/tags/turso.md>), [vector](<https://devfeed.tech/tags/vector.md>)

### AI overview

A tutorial for building a fully local and offline Retrieval-Augmented Generation pipeline using Ollama, Mistral, and Turso. It stores documents and vector embeddings in a SQLite file, generates embeddings locally, and retrieves relevant content without cloud calls or network access.

### Source excerpt

Build a fully local RAG pipeline with Ollama, Mistral, and Turso. Store data and vector embeddings in one SQLite file, with no cloud calls and no network.

## Controlling Language Model Generation with NVIDIA's LogitsProcessorZoo

DevFeed: [Controlling Language Model Generation with NVIDIA's LogitsProcessorZoo](<https://devfeed.tech/articles/controlling-language-model-generation-with-nvidia-s-logitsprocessorzoo-7343.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/logits-processor-zoo>)

Author: Aritra Roy Gosthipaty; Ahmet Erdem

Published: 2024-12-23T00:00:00Z

Content type: article

Language: en

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

Topics: [Language models](<https://devfeed.tech/topics/language-models.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [AI Development](<https://devfeed.tech/topics/ai-development.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [generation](<https://devfeed.tech/tags/generation.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [llm](<https://devfeed.tech/tags/llm.md>), [model](<https://devfeed.tech/tags/model.md>), [models](<https://devfeed.tech/tags/models.md>), [modular](<https://devfeed.tech/tags/modular.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open](<https://devfeed.tech/tags/open.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>), [workflows](<https://devfeed.tech/tags/workflows.md>)

### AI overview

This article explains how logit processing controls language model generation by modifying prediction scores before they become token probabilities. It introduces Hugging Face's LogitsProcessor API and NVIDIA's modular LogitsProcessorZoo, highlighting use cases such as sequence-length control, key-phrase enforcement, and multiple-choice guidance.

### Source excerpt

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

## 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.

[Next page](<https://devfeed.tech/tags/text-generation.md?cursor=WyIyMDI0LTExLTE0VDAwOjAwOjAwKzAwOjAwIiwgIjA3N2YwMzNhLTg5YTAtNDIzZS1hZTRlLTdmMDY4MmQwZGQ4ZSJd>)