---
title: "LLMs"
description: "673 posts about LLMs, summarised, each linking to the original."
---

# LLMs
> 673 posts about LLMs, summarised, each linking to the original.

## Articles

### [Supercharge your OCR Pipelines with Open Models](https://yomu.fyi/post/supercharge-your-ocr-pipelines-with-open-models.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: merve, Aritra Roy Gosthipaty, Daniel van Strien, Hynek Kydlicek, Andres Marafioti, Vaibhav Srivastav, Pedro Cuenca
- Published: Oct 21, 2025
- Type: Explainer

Modern vision-language OCR models can transcribe handwritten text, multiple scripts, mathematical expressions, and chemical formulas while also interpreting tables, charts, and images. The guide compares open OCR models and explains how output choices—including DocTags, HTML, Markdown, and structured JSON—affect digital reconstruction, LLM input, and programmatic processing. It also covers locality awareness through anchors or bounding boxes, prompt-based task switching, fine-tuning versus out-of-the-box use, and factors for selecting a model. Practical deployment options include local or managed inference, vLLM, Hugging Face Endpoints, and batch scripts that add OCR results to datasets. The discussion extends document AI beyond transcription to visual retrieval and direct document question answering with vision-language models, especially where converted text can lose layout or visual context.


### [Unlock the power of images with AI Sheets](https://yomu.fyi/post/unlock-the-power-of-images-with-ai-sheets.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Ame Vi, Daniel Vila, Francisco Aranda, Damián Pumar, Leandro von Werra, Thomas Wolf
- Published: Oct 21, 2025
- Type: Announcement

Hugging Face released an update to AI Sheets, an open-source tool for transforming and enriching datasets without code. The update adds vision support powered by Inference Providers, enabling users to upload images, extract structured data, generate visuals, and perform image edits in a spreadsheet interface. Each column can execute AI actions configured with custom prompts and selected models. For text extraction and reasoning tasks, AI Sheets supports vision language models including Qwen/Qwen2.5-VL-7B-Instruct and Qwen/Qwen3-VL-235B-A22B-Reasoning, while image transformations use models like Qwen-Image-Edit. Users can iterate on prompts, provide feedback using thumbs-up ratings to create few-shot examples, and export completed datasets to the Hugging Face Hub, CSV, or Parquet.


### [AI Interview Success: An Interviewer's Inside Guide](https://yomu.fyi/post/ai-interview-success-an-interviewer-s-inside-guide.md)
- Company: [Canva](https://yomu.fyi/company/canva.md)
- Author: Karl Hörnlund
- Published: Oct 20, 2025
- Type: Explainer

Canva conducts AI-assisted programming interviews designed to evaluate candidates' engineering fundamentals amplified by artificial intelligence tools. Successful candidates structure their interviews across planning, development, and discussion phases, clarifying architectural assumptions before writing code and critically reviewing AI-generated output. Interviewers evaluate engineering problem-solving, technical depth, code ownership, and the effective collaboration with AI assistants rather than isolated prompting mechanics or rushed feature completion. Ineffective strategies include treating models as hands-off solution generators, rushing unvetted features, and prioritizing demonstrations of advanced prompting over core technical judgment. Preparation involves setting up project structures in advance, exploring requirements aloud, and remaining ready to justify architectural trade-offs and production scalability.


### [Google Cloud C4 Brings a 70% TCO improvement on GPT OSS with Intel and Hugging Face](https://yomu.fyi/post/google-cloud-c4-brings-a-70-tco-improvement-on-gpt-oss-with-intel-and.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Jiqing.Feng, Matrix Yao, Ke Ding, Ilyas Moutawwakil
- Published: Oct 16, 2025
- Type: Benchmark

Intel and Hugging Face benchmarked OpenAI GPT OSS text generation to evaluate performance on Google Cloud C4 virtual machines powered by Intel Xeon 6 processors against previous-generation C3 instances. The benchmark evaluated the unsloth/gpt-oss-120b-BF16 Mixture of Experts model across batch sizes from 1 to 64 using bfloat16 precision, static KV cache, and SDPA attention. To eliminate redundant computation, Hugging Face merged an optimization directing each expert sub-network to process only its routed tokens rather than all tokens. Testing demonstrated that C4 instances deliver 1.4x to 1.7x higher throughput per vCPU compared to C3 instances. At a batch size of 64, C4 achieved a 1.7x Total Cost of Ownership improvement alongside lower hourly pricing.


### [Nemotron-Personas-India: Synthesized Data for Sovereign AI](https://yomu.fyi/post/nemotron-personas-india-synthesized-data-for-sovereign-ai.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Kiran Praveen, Utkarsh Vaidya, Evan A, Lipika Ramaswamy, Dhruv Nathawani, Dane Corneil, Yev Meyer
- Published: Oct 13, 2025
- Type: Announcement

Most open artificial intelligence datasets reflect Western norms and English-only contexts, limiting model effectiveness across India's multilingual and multi-script landscape. To address this shortage of culturally grounded training data, NVIDIA released Nemotron-Personas-India under a CC BY 4.0 license. The synthetic dataset contains 21 million personas across 3 million records spanning 7.7 billion tokens in English, Devanagari Hindi, and Latin-script Hindi. The generation pipeline utilized NeMo Data Designer, an Apache-2.0 licensed Probabilistic Graphical Model for demographic grounding against 2011 Census distributions, and GPT-OSS-120B for narrative text. All records are entirely synthetic to eliminate re-identification risks while enabling fine-tuning for region-aware AI agents and domain-specific copilots.


### [Understanding Etsy’s Vast Inventory with LLMs](https://yomu.fyi/post/understanding-etsy-s-vast-inventory-with-llms.md)
- Company: [Etsy](https://yomu.fyi/company/etsy.md)
- Author: Vipul Setty
- Published: Oct 13, 2025
- Type: Problem & solution

Etsy’s seller-created inventory spans more than 100 million items and thousands of categories, making it difficult to capture attributes such as color, material, or niche properties without global SKUs. Because sellers often provide descriptions, titles, and photos instead of complete structured fields, Etsy uses LLMs to distill unstructured listing data into attributes for search filters, comparisons, and other experiences. The pipeline combines seller data, expert-selected few-shot examples, taxonomy logic, and category rules; parallel prompts are routed through LiteLLM, then responses are validated with Pydantic dataclasses and monitored with tracing, metrics, alerts, and performance checks. Human-reviewed silver labels support evaluation, while target categories increased complete attribute coverage from 31% to 91%, and inferred attributes improved search-filter engagement and overall post-click conversion.


### [Arm will be @ PyTorch Conference, Join Us!](https://yomu.fyi/post/arm-will-be-pytorch-conference-join-us.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: EricSondhi
- Published: Oct 10, 2025
- Type: Announcement

Arm announced its schedule and offerings for the PyTorch Conference on October 22–23, focusing on building and deploying AI applications using PyTorch and ExecuTorch. The company is hosting one-on-one workshops with design experts to improve product usability and introduce Yellow Teaming practices for mitigating fairness, transparency, privacy, and security risks. Additionally, Arm is running 30-minute Voice of the Developer sessions to gather feedback on real-world challenges, including migrating from NVIDIA or x86 to Arm, debugging models at scale, and running large language models on edge devices. At Booth P1, attendees can view interactive demonstrations featuring neural graphics training, speech recognition, audio generation, vLLM, Mixture of Experts, and ExecuTorch edge deployments.


### [BigCodeArena: Judging code generations end to end with code executions](https://yomu.fyi/post/bigcodearena-judging-code-generations-end-to-end-with-code-executions.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Terry Yue Zhuo
- Published: Oct 7, 2025
- Type: Announcement

Assessing AI-generated code via static inspection is error-prone and fails to reflect whether code functions correctly across real-world environments. BigCodeArena addresses this evaluation bottleneck as a human-in-the-loop platform that executes model outputs in real time inside isolated sandbox environments across ten programming languages and eight execution frameworks. Users interact directly with live applications, refine implementations through multi-turn conversations, and submit pairwise preference votes to generate Elo ratings via the Bradley-Terry model. In five months of community testing covering over 14,000 conversations, proprietary models including o3-mini and o1-mini led overall Elo rankings, while open models like Qwen2.5 and Llama-3.3-70B trailed closed competitors. The project has open-sourced its full evaluation pipelines, conversation datasets, and related benchmarks to support community-driven code generation assessment.


### [Smarter AI Search, Powered by MongoDB Atlas and Pureinsights](https://yomu.fyi/post/smarter-ai-search-powered-by-mongodb-atlas-and-pureinsights.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Kamran Khan (CEO, Pureinsights), Prasad Pashte
- Published: Oct 1, 2025
- Type: Announcement

MongoDB Atlas has announced the general availability of its integration with the Pureinsights Discovery Platform to deliver a unified keyword, vector, and generative search experience. The combined solution pairs MongoDB Atlas Search for standard text matching with MongoDB Atlas Vector Search and Voyage AI embeddings to interpret ambiguous or multilingual queries. Pureinsights provides the orchestration layer that ingests content, coordinates retrieval, and integrates large language models such as GPT-4 to generate cited responses. Users can customize generated outputs according to preferred technical depth, length, language, and role-specific requirements. The architecture enables enterprises to apply retrieval-augmented generation and semantic search across technical documentation, community forums, and internal knowledge bases.


### [Introducing RTEB: A New Standard for Retrieval Evaluation](https://yomu.fyi/post/introducing-rteb-a-new-standard-for-retrieval-evaluation.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Frank Liu, Kenneth Enevoldsen, Solomatin Roman, Isaac Chung, Tom Aarsen, Fődi, Zoltán
- Published: Oct 1, 2025
- Type: Announcement

Public embedding benchmarks often fail to measure true generalization because training sets overlap with public evaluation data, rewarding memorization over general search quality. The Retrieval Embedding Benchmark (RTEB) beta introduces a standardized evaluation framework designed for real-world enterprise retrieval applications. To prevent overfitting while preserving transparency, RTEB combines fully public datasets with private evaluation sets managed exclusively by MTEB maintainers. The suite spans 20 languages across domains like healthcare, law, finance, and code, measuring ranked retrieval quality using NDCG@10 on datasets of at least 1,000 documents. While currently limited to text-only retrieval and containing repurposed question-answering datasets, the benchmark provides clear signals when models degrade on unseen data.


### [Top Considerations When Choosing a Hybrid Search Solution](https://yomu.fyi/post/top-considerations-when-choosing-a-hybrid-search-solution.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Jose Parra
- Published: Sep 30, 2025
- Type: Explainer

Natural language queries and generative AI workflows like retrieval-augmented generation have exposed the limitations of standalone vector search, particularly for tokens outside an embedding model's training corpus. Hybrid search addresses these gaps by merging traditional lexical precision with dense vector context. Platforms combine these modalities using techniques such as reciprocal rank fusion and relative score fusion, often complemented by downstream cross-encoders or learning-to-rank models. Architecture choices center on the trade-offs between lexical-first systems using inverted indexes and vector-first platforms employing sparse vectors. MongoDB has integrated vector search indexes alongside its existing lexical indexes, releasing native hybrid search functions across Atlas, Community Edition, and Enterprise Server to unify operational data and search retrieval.


### [Accelerating Qwen3-8B Agent on Intel® Core™ Ultra with Depth-Pruned Draft Models](https://yomu.fyi/post/accelerating-qwen3-8b-agent-on-intel-coretm-ultra-with-depth-pruned-dr.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Igor Margulis, Ofir Zafrir, Shira Guskin, Guy Boudoukh, Pedro Cuenca
- Published: Sep 29, 2025
- Type: Problem & solution

Local agentic AI workflows generate multi-step reasoning traces that increase token counts and strain real-time inference on personal computers. To accelerate execution, researchers implemented speculative decoding using OpenVINO.GenAI on an Intel Lunar Lake integrated GPU, pairing a 4-bit Qwen3-8B target model with a smaller Qwen3-0.6B draft model. By measuring angular distance across layers, the team pruned six of the draft model's twenty-eight layers to minimize draft latency and fine-tuned it on synthetic data generated from 500,000 prompts. This depth-pruned draft elevated speculative decoding throughput from a 1.3x speedup to a 1.4x speedup over the standalone baseline. Finally, deploying the optimized model alongside the Hugging Face smolagents framework allowed local agents to perform tool invocation and code generation tasks efficiently.


### [VibeGame: Exploring Vibe Coding Games](https://yomu.fyi/post/vibegame-exploring-vibe-coding-games.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Dylan Ebert
- Published: Sep 29, 2025
- Type: Problem & solution

Vibe coding games often fails as projects grow because large context sizes degrade model performance. After evaluating Roblox MCP, Unity MCP, and open web libraries with Claude Code, the author found web tooling offered the strongest model proficiency but lacked high-level game abstractions. To bridge this gap, the author created VibeGame, an open-source declarative game engine built on three.js, rapier, and bitecs. VibeGame uses an Entity-Component-System architecture, XML-like markup, and bundled documentation in an llms.txt file to keep project context lean. While effective for basic physics and rendering mechanics, the engine requires further development for complex features like inventories, multiplayer, and interactions.


### [Swift Transformers Reaches 1.0 – and Looks to the Future](https://yomu.fyi/post/swift-transformers-reaches-1-0-and-looks-to-the-future.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Pedro Cuenca, Christopher Fleetwood, Mattt, Vaibhav Srivastav
- Published: Sep 26, 2025
- Type: Announcement

swift-transformers has released version 1.0 to provide a stable foundation for running local models on Apple Silicon platforms. The library supplies components missing from Core ML and MLX, focusing heavily on tokenization, chat templates, and Hugging Face Hub integration. In this major release, Tokenizers and Hub become standalone top-level modules, enabling developers to import only the components they require. The update also integrates an updated swift-jinja library for faster template processing, adopts Modern Core ML stateful models and MLTensor APIs, removes unnecessary CLI dependencies, and adds full Swift 6 support. Future development will prioritize deeper integration with MLX frameworks and agentic workflows like MCP.


### [Build AI Agents Worth Keeping: The Canvas Framework](https://yomu.fyi/post/build-ai-agents-worth-keeping-the-canvas-framework.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Mikiko Bazeley
- Published: Sep 23, 2025
- Type: Explainer

Enterprise AI agent initiatives frequently stall after pilot phases due to technology-first thinking, governance gaps, infrastructure complexity, and poor alignment with business needs. To bridge this divide, development teams are shifting away from data-first pipelines toward a product-first methodology structured as product, agent, data, and model. The Canvas framework provides a phased workflow moving from quick proof-of-concept validation to model orchestration and operational hardening. In Phase 4, developers focus on API management, external provider orchestration, cost optimization, and evaluation pipelines. Phase 5 adds necessary governance, compliance, user experience, and security layers required to transform working agent prototypes into sustainable production deployments.


### [Smol2Operator: Post-Training GUI Agents for Computer Use](https://yomu.fyi/post/smol2operator-post-training-gui-agents-for-computer-use.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Amir Mahla, merve, Sergio Paniego, Vaibhav Srivastav, Lewis Tunstall
- Published: Sep 23, 2025
- Type: Problem & solution

Lightweight vision-language models often lack the precise visual grounding and action execution capabilities required for graphical user interface automation. To resolve this deficiency, the authors constructed a post-training pipeline that evolves base models into agentic GUI coders through supervised fine-tuning. The process standardizes disparate mobile and desktop dataset action spaces into a unified functional format utilizing normalized coordinates relative to image size. Training is structured into two sequential phases that first establish basic visual perception and subsequently introduce explicit reasoning steps with masked assistant loss. On the ScreenSpot-v2 perception benchmark, fine-tuning SmolVLM2-2.2B-Instruct increased element localization accuracy to 61.71 percent, while a compact 460-million-parameter model achieved approximately 58 percent accuracy.


### [Simplify AI-Driven Data Connectivity With MongoDB and MCP Toolbox](https://yomu.fyi/post/simplify-ai-driven-data-connectivity-with-mongodb-and-mcp-toolbox.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Venkatesh Shanbhag, Yang Li, Kurtis Van Gent
- Published: Sep 22, 2025
- Type: Announcement

Organizations building generative AI applications require simplified, standardized connections to an expanding ecosystem of enterprise databases. MCP Toolbox for Databases, an open-source Model Context Protocol server based on Anthropic's standardized protocol, now supports MongoDB Atlas to streamline connections to generative AI systems. This new integration allows gen AI agents to execute standard CRUD operations alongside advanced transformations and computations using the MongoDB aggregation framework. Through flexible configuration files, developers can configure MongoDB Atlas as both a data source and a sink by defining parameters like connection URIs, target datasets, filters, projections, and toolsets. By linking flexible document data with cross-database query capabilities, enterprises can unify results and automate multi-source AI workflows.


### [SyGra: The One-Stop Framework for Building Data for LLMs and SLMs](https://yomu.fyi/post/sygra-the-one-stop-framework-for-building-data-for-llms-and-slms.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Bidyapati Pradhan, Vipul Mittal, Amit Kumar Saha, Surajit Dasgupta
- Published: Sep 22, 2025
- Type: Problem & solution

Training and aligning large language models and small language models requires datasets tailored for complex reasoning, preference tuning, multi-turn questioning, and format conversion. To eliminate the need for bespoke data preparation scripts, SyGra provides a low-code and no-code Python framework for automated dataset creation, transformation, and alignment. The library integrates with diverse inference backends, including vLLM, Hugging Face TGI, Triton, and Ollama, allowing teams to focus on prompt engineering rather than pipeline infrastructure. It supports data workflows ranging from converting PDFs and knowledge bases into question-answering pairs to filtering low-quality samples and generating Direct Preference Optimization datasets. These plug-and-play workflows reduce manual curation effort while accelerating model fine-tuning and retrieval-augmented generation pipelines.


### [Gaia2 and ARE: Empowering the community to study agents](https://yomu.fyi/post/gaia2-and-are-empowering-the-community-to-study-agents.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Clémentine Fourrier, Grégoire Mialon, Maxime Lecanu, Pierre Andrews, Adrien Carreira, frere thibaud, Avijit Ghosh, Romain Froger, Dheeraj Mekala, Caroline Pascal, Ulyana Piterbarg
- Published: Sep 22, 2025
- Type: Benchmark

Existing AI agent evaluation environments are often tightly coupled to specific tasks and fail to model real-world challenges such as API failures, spontaneous events, and asynchronous conditions. To address this limitation, the Gaia2 benchmark and the Meta Agents Research Environments (ARE) framework introduce interactive read-and-write evaluation. Gaia2 incorporates 1,000 human-created scenarios spanning multi-step execution, cross-source search, ambiguity handling, adaptability, temporal reasoning, agent collaboration, and noise tolerance. Using a simulated smartphone interface equipped with 101 tools, evaluations showed that GPT-5 with high reasoning scored highest overall, while Kimi K2 was the leading open-source model. The results demonstrated that instruction following and search do not reliably predict performance on closer-to-real-world tasks.


### [Scaleway on Hugging Face Inference Providers 🔥](https://yomu.fyi/post/scaleway-on-hugging-face-inference-providers.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Guillaume Noale, Franck Pagny, Fred Bardolle, Guillaume Calmettes, Constance Morales, Célina Hanouti, Julien Chaumond, Simon Brandeis, Lucain Pouget
- Published: Sep 19, 2025
- Type: Announcement

Scaleway is integrated as a supported serverless Inference Provider on the Hugging Face Hub, expanding model deployment options across Hub model pages and official client SDKs for JavaScript and Python. Operating out of European data centers located in Paris, France, Scaleway Generative APIs host open-weight models including gpt-oss, Qwen3, DeepSeek R1, and Gemma 3 with structured outputs, function calling, multimodal processing, and sub-200ms first-token response times. Developers can route inference requests using their Hugging Face tokens or supply direct Scaleway API keys. Billing for routed requests charges standard provider rates starting at €0.20 per million tokens without markups, while direct requests bill to Scaleway accounts. Hugging Face PRO subscribers receive two dollars of monthly inference credits applicable across providers.


[Newer posts](https://yomu.fyi/topic/llm/page/28.md) · [Older posts](https://yomu.fyi/topic/llm/page/30.md)