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June 5, 2025

How to Install and Run Xiaomi MiMo-VL Locally

We are living in times when vision-language models (VLMs) are rapidly evolving with each of them having their own pros and cons and uniqueness to stand out. MiMo-VL-7B, recently launched by Xiaomi, is now trending on Hugging Face and stands out as a compact yet remarkably capable model that pushes the boundaries of multi-modal reasoning. At just 7B parameters, MiMo-VL-7B integrates a native-resolution ViT encoder that captures fine-grained visual detail, an efficient MLP projector for cross-modal alignment, and the MiMo-7B language model optimized specifically for complex reasoning. Its development spans a rigorous two-phase training pipeline: a four-stage pretraining phase that results in the SFT variant, and a post-training phase introducing Mixed On-policy Reinforcement Learning (MORL), which blends perception, grounding, reasoning, and alignment objectives in a unified framework. This thoughtful design enables MiMo-VL-7B to deliver state-of-the-art performance across general understanding, GUI tasks, and multi-modal reasoning, often outperforming larger open-source models across all benchmarks. This model is perfect if you’re building a general-purpose VLM system or need deep reasoning over images, videos, and text.

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June 4, 2025

How to Install and Run Sarvam – M Locally?

Sarvam-M is a multilingual, hybrid-reasoning, text-only language model built on Mistral-Small. This advanced model offers exceptional improvements over its base counterpart, showcasing a +20% enhancement in Indian language benchmarks, a +21.6% improvement on math tasks, and a +17.6% boost in programming capabilities. Sarvam-M excels particularly in scenarios that involve Indian languages and mathematics, achieving impressive performance, including an +86% improvement on romanized Indian language GSM-8K benchmarks. With a hybrid thinking mode, Sarvam-M supports both complex reasoning tasks such as coding and mathematical problem-solving, as well as general-purpose conversation in a seamless manner. It is specifically designed to handle both Indic scripts and romanized versions, making it a versatile solution for multilingual environments, particularly for Indian users. The model’s superior reasoning abilities make it ideal for tasks requiring high accuracy in logical thinking, math, and coding. It can easily switch between “think” mode for complex tasks and “non-think” mode for efficient conversation.

June 3, 2025

How to Install Fathom-R1-14B: The Most Efficient SOTA Math Reasoning LLM

In a landscape where reasoning-focused LLMs often demand sky-high compute budgets and ultra-long context lengths, Fathom-R1-14B by Fractal AI stands out as an outstanding alternative. It is developed as part of the ambitious IndiaAI Mission to build the nation’s first Large Reasoning Model (LRM), and this 14B parameter model delivers state-of-the-art performance on Olympiad-grade mathematical reasoning tasks – all within a modest 16K context window and a shockingly low $499 post-training budget. Unlike models that rely on 32k+ token inference and massive fine-tuning pipelines, Fathom-R1-14B achieves scores of 52.71% on AIME25 and 35.26% on HMMT25, significantly outperforming comparable open-source models like o3-mini and Light-R1-14B. Moreover, it competes with proprietary giants like o4-mini-low, proving that efficient reasoning and accessible compute no longer need to be at odds. What’s more, Fractal AI is open-sourcing everything, from weights and recipes to datasets, empowering researchers, students, and indie developers to play around, build, and scale with confidence.

June 2, 2025

How to Install NVIDIA AceReason-Nemotron-14B Locally?

NVIDIA’s AceReason-Nemotron-14B is a powerful open-source model designed specifically for solving complex math and code problems with clarity and structure. What sets this model apart is its training approach — it’s built entirely through reinforcement learning, not just on general language, but on dedicated reasoning tasks. Starting from the distilled DeepSeek-R1-Qwen foundation, AceReason was trained in two clear stages: first on math-only problems to sharpen its calculation and symbolic reasoning, and then on code-only tasks to strengthen its problem-solving and logic handling. This two-stage refinement pays off — the model consistently outperforms many larger models on tough benchmarks like AIME and LiveCodeBench. Whether you’re testing math olympiad problems, writing recursive functions, or debugging algorithm logic, AceReason-Nemotron delivers thoughtful, structured answers that are often boxed with a final conclusion — just like a math student showing their work. It’s built for developers, researchers, and technical learners who want accuracy, depth, and structure in reasoning-heavy tasks.

May 31, 2025

How to Install and Run Chatterbox Locally

If you’ve been searching for a powerful, open-source text-to-speech (TTS) model that doesn’t compromise on quality or flexibility, Chatterbox might just blow your mind. Developed by Resemble AI, Chatterbox is the first production-grade TTS model that’s not only free to use but also outperforms industry giants like ElevenLabs in direct listening tests. It is built on a 0.5B Llama backbone and trained on an impressive 500,000 hours of cleaned speech data, and this model delivers ultra-stable, high-fidelity speech synthesis with remarkable control. Its standout feature – Emotion exaggeration and intensity control, allowing creators to fine-tune the expressiveness of generated voices, something never before seen in the open-source landscape. If you’re designing interactive AI agents, adding emotion to videos and vlogs, or building immersive animated characters, Chatterbox makes your synthetic voices feel real, dynamic, and production-ready. With alignment-informed inference, a handy voice conversion script, and watermarked outputs for traceability, this model is ready for both experimentation and scale.

May 29, 2025

A Step-by-Step Guide to Install DeepSeek-R1-0528 Locally with Ollama, vLLM or Transformers

The upgraded DeepSeek-R1-0528 isn’t just a minor revision, it’s a significant achievement in the open-source AI industry as it’s successfully outperforming some very well known top notch closed source models like o3 from OpenAI and many others. This new version is designed with smarter algorithms and backed by larger-scale computation, which sharpens its ability to handle complex tasks in mathematics, programming, and logical inference. It’s shown dramatic benchmark improvements: AIME 2025 accuracy jumped from 70% to 87.5%, and models now reason with double the token depth, up to 23K per question. With a reduced hallucination rate, enhanced function-calling, and impressive performance across coding, it’s giving bleeding edge competition to industry giants like OpenAI’s O3 and Gemini 2.5 Pro. Its distilled version, DeepSeek-R1-0528-Qwen3-8B, even surpasses 30B+ parameter giants while staying lightweight and efficient, making it one of the most promising reasoning LLMs available today.

May 29, 2025

Step-by-Step Guide to Run Llama 4 Locally with Tool Calling Enabled

The open-source model ecosystem is moving fast, and Llama 4 is one of the most powerful and flexible model families available today. Built with native multimodality, Mixture-of-Experts (MoE) architecture, and support for tool calling, Llama 4 opens up a world of possibilities across text, code, and vision. With the recent release of Ollama v0.8, developers can now leverage real-time streaming responses and tool invocation directly from their GPU Virtual Machines. Whether you’re building assistants, agents, or research tools, the combination of Llama 4 and Ollama makes it possible to run highly capable models locally with precision and control.

May 28, 2025

Zed + Ollama + LLMs on a GPU VM: The Ultimate Local Dev Setup for Serious Coders

Zed is a next-generation code editor built from the ground up in Rust for ultimate performance. Whether you’re working solo or collaborating with your team in real time, Zed delivers a buttery-smooth coding experience — from instant startup times to zero-lag typing. With native support for Git, Jupyter, terminals, and remote development, it’s tailored for modern workflows. Zed also integrates deeply with the latest AI assistants, letting you generate, transform, and review code effortlessly through agentic editing and inline intelligence — all while keeping you in control.

May 27, 2025

How to Install BAGEL by ByteDance: The Vision Language Model That Can Do It All

If you’ve been following the rapid evolution of multimodal AI, then you might have heard about the latest trending Gen AI model, that is, ByteDance’s BAGEL. It is one of its kind omni vision language model, designed for everything from image generation, editing, to understanding, with just 7B parameters. Bagel gives tough competition to its top-tier rivals and open-source VLMs like Qwen2.5-VL, DeepSeek’s Janus Pro and FLUX-1-dev. It also delivers image generation quality on par with specialist models like SD3. Just give one prompt in natural language, like “add sunglasses to my picture” or “replace the background with sunset view” and it’ll do that for you in seconds. For enthusiasts looking to generate visuals from text, editing an image with high precision, or reasoning across multimodal sequences, BAGEL brings state-of-the-art results and unprecedented flexibility in one unified model.

May 26, 2025

Void + Ollama + LLMs: How I Turned My Code Editor into a Full-Blown AI Workbench

Void is an open-source alternative to Cursor — a fast, privacy-first AI code editor built on top of VS Code. With Void, you can run any LLM (local or cloud), use advanced AI agents on your codebase, track changes with checkpoints, and integrate seamlessly with providers like Ollama, Claude, DeepSeek, OpenAI, and more — all without your data ever leaving your machine.

May 24, 2025

Build Medical AI with Google’s MedGemma 4B & 27B: A Complete Installation Guide

New AI innovations are happening almost everyday, but AI for healthcare, is still a sector rarely touched by tech innovators. However, Google has taken this initiative to revolutionize the Healthcare sector with its latest MedGemma models launched in the recent Google I/O Connect. These models stand out as modern tools for building intelligent medical applications. MedGemma is a family of models based on the powerful Gemma 3 architecture, fine-tuned to handle the intricacies of both medical text and image data. The MedGemma 4B variant is a multimodal model, combining a SigLIP image encoder, pre-trained on de-identified chest X-rays, dermatology, ophthalmology, and histopathology images, with a robust language model trained on a similarly rich set of medical data. These models are perfect for developer aiming to build stuff like diagnostic assistant, radiology report generator, or a dermatology triage tool. MedGemma 4B offers a powerful instruction-tuned variant to get started quickly, along with a pre-trained option for deeper experimentation. Meanwhile, the MedGemma 27B model focuses purely on text-based medical reasoning, making it ideal for tasks like clinical summarization, decision support, or answering complex medical queries with precision. Optimized for inference efficiency, it provides impressive performance straight out of the box on a range of healthcare benchmarks. If you’re a medical researcher, a healthcare AI startup, or an ML engineer looking to explore domain-specific models, MedGemma delivers a solid foundation for innovation, and this guide will show you the complete step-by-step process on how to install and run both variants locally or with GPUs in minutes.

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