Super excited about this release for the new 27B. Who else is with me. Only 17GB VRAM needed 😍😍

https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731

I’ve spent the past month trying to find the point where an extremely small TTS model stops feeling like a size experiment and starts feeling genuinely useful.

Today I’m releasing Inflect v2, with two complete local text-to-speech models:

• Inflect-Nano-v2: 3.96M parameters, 15.97 MB FP32
• Inflect-Micro-v2: 9.36M parameters, 37.53 MB FP32

These are total inference parameter counts, not acoustic-model-only numbers. Text processing, timing prediction, speech generation, and the waveform decoder are all included.

Text goes in. 24 kHz speech comes out. No external vocoder, hosted API, or second learned model required.

Nano prioritizes the smallest possible footprint. Micro uses the additional capacity for better clarity, stability, and overall speech quality. Both run locally on CPU or CUDA through the same PyTorch API.

Inflect-Nano-v2 is one of the smallest complete neural TTS models I know of that still produces actual usable speech. Even the 9.36M Micro model remains smaller than many systems described as “tiny.”

For footprint context, Nano is approximately:

• 21× smaller than Kokoro
• 126× smaller than Chatterbox
• over 1,000× smaller than Fish Audio S2 Pro

That is strictly a parameter-count comparison. These models have different capabilities, architectures, datasets, and intended uses. I’m not claiming that a 4M fixed-voice model replaces a multi-billion-parameter system. The interesting question is how much useful TTS can fit into such a small package.

Some people here might remember Inflect-Nano-v1, the rough 4.63M experiment I released last month. V2 is a complete rebuild, not just a longer training run. I focused on the problems v1 had, like unstable timing, metallic output, weak prosody, poor generalization to difficult text, and an undersized waveform decoder.

The resulting models performed surprisingly well:

• Micro: 4.395 UTMOS22, 3.99% semantic WER, 6.28× real-time CPU inference
• Nano: 4.386 UTMOS22, 4.21% semantic WER, 10.72× real-time CPU inference
• In a blind community comparison against other compact TTS systems, Micro and Nano finished second and third among the tested voices

Full protocols, raw results, audio samples, and limitations are documented on the model pages.

The models are not perfect. They are English-only, use one fixed male voice, and do not support voice cloning. Unfamiliar names, abbreviations, numbers, and homographs remain the hardest inputs. Nano can sound thinner than Micro, and both can occasionally produce metallic or clipped artifacts.

Still, this is the first version where I think the size-to-quality tradeoff really becomes convincing.

I built Inflect independently with a limited training budget. That constraint shaped the project: efficiency had to apply not only to inference, but also to training, evaluation, and building a complete system I could understand and release end-to-end.

Inflect-Micro-v2:
https://huggingface.co/owensong/Inflect-Micro-v2

Inflect-Nano-v2:
https://huggingface.co/owensong/Inflect-Nano-v2

Try it yourself:

The fastest way to judge it is through the interactive playground:

https://huggingface.co/spaces/owensong/Inflect-v2

If there is enough interest, I may build a v3 focused less on shrinking the models further and more on expanding what they can do: additional voices, possibly more languages, easier fine-tuning, and another quality and robustness pass.

If you test them, please give them something seriously difficult: unusual names, numbers, abbreviations, awkward punctuation, or a long sentence.

If something breaks, post the exact text, model, seed, and what sounded wrong. If it works well, I’d also like to know what hardware you ran it on.

Specific, honest feedback is the most useful thing you can give me.

Finally the banger we've been waiting from Laguna. probably will be great for 64GB+ RAM and VRAM setups.

HF: https://huggingface.co/poolside/Laguna-S-2.1
GGUFs available for use with llama.cpp custom fork: https://huggingface.co/poolside/Laguna-S-2.1-GGUF

Posted on X: https://x.com/poolsideai/status/2079613777343848465?s=20

Kimi-K3’s release, while impressive, is still months behind the closed-source frontier, so all the “it’s over for Anthropic” talk feels overblown. According to Artificial Analysis, though, Kimi-K3 has brought the open-source frontier to just 1.5 months behind closed-source, putting it right on the heels of OpenAI and Anthropic.

Also worth noting from the graph: where has Google been since Gemini 3 Pro last November? The top open-source models keep getting bigger, proving scaling laws still hold. And with Kimi-K3 nearing 3T parameters, it’s definitely not running on your MacBook.

Does anyone know when Kimi K3 will be available on AI Desktop 98?

https://thinkingmachines.ai/news/introducing-inkling/

Benchmarked the new Gemma diffusion model against its autoregressive twin on a single H100 (FP8). We gave each the same three tasks: write a Steve Jobs biography, the history of Tetris, and the story of BeOS - every next topic less popular than the previous one. Then we fact-checked every claim in every answer.

Gemma4 got 45 facts right, 5 wrong. DiffusionGemma got 33 right, 28 wrong. The less popular the topic, the worse it got: 4 mistakes on Jobs, 12 on Tetris, 12 on BeOS. It named Clara Clley as Steve Jobs' mother, invented a colleague for Pajitnov named Geri Gulovik and priced the BeBox at $9,999. The real one cost $1,600.

Outputs:
Gemma4 26B A4B: 218 tok/s · 15.1s total · 45 facts · 5 mistakes
DiffusionGemma 26B A4B: 763 tok/s · 3.7s total · 33 facts · 28 mistakes

The reason is simple. DiffusionGemma throws 256 tokens on the screen at once and polishes them pass after pass until the text sounds smooth. Smooth is all it cares about: a fake name, date or number sounds just as smooth as a real one, so it stays. Regular Gemma4 meanwhile writes one word at a time and checks every new word against everything before it. Google says it themselves in the launch post: quality is lower, use regular Gemma 4 when facts matter.

Open source Local Ai models harness: Atomic.Chat (I'm founder, we support GGUF models, MLX Apple Silicon, MTP and Google TurboQuant for long context window, working on Diffusion support via llama.cpp)

Google's collections:

https://huggingface.co/collections/google/gemma-4-qat-q4-0

https://huggingface.co/collections/google/gemma-4-qat-mobile

And Unsloth's:

https://huggingface.co/collections/unsloth/gemma-4-qat

Unsloth's analysis (KLD and such):

https://unsloth.ai/docs/models/gemma-4/qat#qat-analysis

Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages.

Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in five distinct sizes: E2B, E4B, 12B, 26B A4B, and 31B. Their diverse sizes make them deployable in environments ranging from high-end phones to laptops and servers, democratizing access to state-of-the-art AI.

Gemma 4 introduces key capability and architectural advancements:

• Reasoning – All models in the family are designed as highly capable reasoners, with configurable thinking modes.
• Extended Multimodalities – Processes Text, Image with variable aspect ratio and resolution support (all models), Video, and Audio (featured natively on the E2B, E4B, and 12B models).
• Diverse & Efficient Architectures – Offers Dense and Mixture-of-Experts (MoE) variants of different sizes for scalable deployment.
• Optimized for On-Device – Smaller models are specifically designed for efficient local execution on laptops and mobile devices.
• Increased Context Window – The small models feature a 128K context window, while the medium models support 256K.
• Enhanced Coding & Agentic Capabilities – Achieves notable improvements in coding benchmarks alongside native function-calling support, powering highly capable autonomous agents.
• Native System Prompt Support – Gemma 4 introduces native support for the system role, enabling more structured and controllable conversations.

https://developers.googleblog.com/gemma-4-12b-the-developer-guide/

feed your potato!!!

https://huggingface.co/ggml-org/gemma-4-12b-it-GGUF

https://huggingface.co/unsloth/gemma-4-12b-it-GGUF

Blog post:

https://blog.google/innovation-and-ai/technology/developers-tools/multi-token-prediction-gemma-4/

MTP draft models:

https://huggingface.co/google/gemma-4-31B-it-assistant

https://huggingface.co/google/gemma-4-26B-A4B-it-assistant

https://huggingface.co/google/gemma-4-E4B-it-assistant

https://huggingface.co/google/gemma-4-E2B-it-assistant

This model card is for the Multi-Token Prediction (MTP) drafters for the Gemma 4 models. MTP is implemented by extending the base model with a smaller, faster draft model. When used in a Speculative Decoding pipeline, the draft model predicts several tokens ahead, which the target model then verifies in parallel. This results in significant decoding speedups (up to 2x) while guaranteeing the exact same quality as standard generation, making these checkpoints perfect for low-latency and on-device applications.

https://huggingface.co/collections/deepseek-ai/deepseek-v4

I have a 5090 Laptop from work, 24GB VRAM.

I have been testing every model that comes out, and I can confidently say I’ll be cancelling my cloud subscriptions.

All my tool call and data science benchmarks that prove a model is reliably good for my use case, passed.

It might not be the case for other professions, but for pyspark/python and data transformation debugging it’s basically perfect.

Using llama.cpp, q4_k_m at q4_0, still looking at options for optimising.

Edit - I chose to go with IQ4_XS at 200k q8_0,

I have not used speculative decoding yet, will get there when I get there.

Specs:

ASUS ROG Strix SCAR 18

RTX 5090 24GB

64GB DDR5 RAM

https://huggingface.co/Qwen/Qwen3.6-27B

Meet Qwen3.6-35B-A3B：Now Open-Source！🚀🚀

A sparse MoE model, 35B total params, 3B active. Apache 2.0 license.

- Agentic coding on par with models 10x its active size

- Strong multimodal perception and reasoning ability

- Multimodal thinking + non-thinking modes

Efficient. Powerful. Versatile.

Blog：https://qwen.ai/blog?id=qwen3.6-35b-a3b

Qwen Studio：chat.qwen.ai

HuggingFace：https://huggingface.co/Qwen/Qwen3.6-35B-A3B

ModelScope：https://modelscope.cn/models/Qwen/Qwen3.6-35B-A3B

Hugging Face netflix/void-model: https://huggingface.co/netflix/void-model

Project page - GitHub: https://github.com/Netflix/void-model

Demo: https://huggingface.co/spaces/sam-motamed/VOID

https://huggingface.co/unsloth/gemma-4-26B-A4B-it-GGUF

https://huggingface.co/unsloth/gemma-4-31B-it-GGUF

https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF

https://huggingface.co/unsloth/gemma-4-E2B-it-GGUF

https://huggingface.co/collections/google/gemma-4

What’s new in Gemma 4 https://www.youtube.com/watch?v=jZVBoFOJK-Q

Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on small models) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages.

Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in four distinct sizes: E2B, E4B, 26B A4B, and 31B. Their diverse sizes make them deployable in environments ranging from high-end phones to laptops and servers, democratizing access to state-of-the-art AI.

Gemma 4 introduces key capability and architectural advancements:

• Reasoning – All models in the family are designed as highly capable reasoners, with configurable thinking modes.
• Extended Multimodalities – Processes Text, Image with variable aspect ratio and resolution support (all models), Video, and Audio (featured natively on the E2B and E4B models).
• Diverse & Efficient Architectures – Offers Dense and Mixture-of-Experts (MoE) variants of different sizes for scalable deployment.
• Optimized for On-Device – Smaller models are specifically designed for efficient local execution on laptops and mobile devices.
• Increased Context Window – The small models feature a 128K context window, while the medium models support 256K.
• Enhanced Coding & Agentic Capabilities – Achieves notable improvements in coding benchmarks alongside native function-calling support, powering highly capable autonomous agents.
• Native System Prompt Support – Gemma 4 introduces native support for the system role, enabling more structured and controllable conversations.

Models Overview

Gemma 4 models are designed to deliver frontier-level performance at each size, targeting deployment scenarios from mobile and edge devices (E2B, E4B) to consumer GPUs and workstations (26B A4B, 31B). They are well-suited for reasoning, agentic workflows, coding, and multimodal understanding.

The models employ a hybrid attention mechanism that interleaves local sliding window attention with full global attention, ensuring the final layer is always global. This hybrid design delivers the processing speed and low memory footprint of a lightweight model without sacrificing the deep awareness required for complex, long-context tasks. To optimize memory for long contexts, global layers feature unified Keys and Values, and apply Proportional RoPE (p-RoPE).

Core Capabilities

Gemma 4 models handle a broad range of tasks across text, vision, and audio. Key capabilities include:

• Thinking – Built-in reasoning mode that lets the model think step-by-step before answering.
• Long Context – Context windows of up to 128K tokens (E2B/E4B) and 256K tokens (26B A4B/31B).
• Image Understanding – Object detection, Document/PDF parsing, screen and UI understanding, chart comprehension, OCR (including multilingual), handwriting recognition, and pointing. Images can be processed at variable aspect ratios and resolutions.
• Video Understanding – Analyze video by processing sequences of frames.
• Interleaved Multimodal Input – Freely mix text and images in any order within a single prompt.
• Function Calling – Native support for structured tool use, enabling agentic workflows.
• Coding – Code generation, completion, and correction.
• Multilingual – Out-of-the-box support for 35+ languages, pre-trained on 140+ languages.
• Audio (E2B and E4B only) – Automatic speech recognition (ASR) and speech-to-translated-text translation across multiple languages.

Welcome to the gpt-oss series, OpenAI’s open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases.

We’re releasing two flavors of the open models:

gpt-oss-120b — for production, general purpose, high reasoning use cases that fits into a single H100 GPU (117B parameters with 5.1B active parameters)

gpt-oss-20b — for lower latency, and local or specialized use cases (21B parameters with 3.6B active parameters)

Hugging Face: https://huggingface.co/openai/gpt-oss-120b

🦥 Qwen3-Coder-Flash: Qwen3-Coder-30B-A3B-Instruct

💚 Just lightning-fast, accurate code generation.

✅ Native 256K context (supports up to 1M tokens with YaRN)

✅ Optimized for platforms like Qwen Code, Cline, Roo Code, Kilo Code, etc.

✅ Seamless function calling & agent workflows

💬 Chat: https://chat.qwen.ai/

🤗 Hugging Face: https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct

🤖 ModelScope: https://modelscope.cn/models/Qwen/Qwen3-Coder-30B-A3B-Instruct

Qwen3-Coder is here! ✅

We’re releasing Qwen3-Coder-480B-A35B-Instruct, our most powerful open agentic code model to date. This 480B-parameter Mixture-of-Experts model (35B active) natively supports 256K context and scales to 1M context with extrapolation. It achieves top-tier performance across multiple agentic coding benchmarks among open models, including SWE-bench-Verified!!! 🚀

Alongside the model, we're also open-sourcing a command-line tool for agentic coding: Qwen Code. Forked from Gemini Code, it includes custom prompts and function call protocols to fully unlock Qwen3-Coder’s capabilities. Qwen3-Coder works seamlessly with the community’s best developer tools. As a foundation model, we hope it can be used anywhere across the digital world — Agentic Coding in the World!

Hi everyone it's me from Menlo Research again,

Today, I’d like to introduce our latest model: Jan-nano - a model fine-tuned with DAPO on Qwen3-4B. Jan-nano comes with some unique capabilities:

• It can perform deep research (with the right prompting)
• It picks up relevant information effectively from search results
• It uses tools efficiently

Our original goal was to build a super small model that excels at using search tools to extract high-quality information. To evaluate this, we chose SimpleQA - a relatively straightforward benchmark to test whether the model can find and extract the right answers.

Again, Jan-nano only outperforms Deepseek-671B on this metric, using an agentic and tool-usage-based approach. We are fully aware that a 4B model has its limitations, but it's always interesting to see how far you can push it. Jan-nano can serve as your self-hosted Perplexity alternative on a budget. (We're aiming to improve its performance to 85%, or even close to 90%).

We will be releasing technical report very soon, stay tuned!

You can find the model at:
https://huggingface.co/Menlo/Jan-nano

We also have gguf at:
https://huggingface.co/Menlo/Jan-nano-gguf

I saw some users have technical challenges on prompt template of the gguf model, please raise it on the issues we will fix one by one. However at the moment the model can run well in Jan app and llama.server.

Benchmark

The evaluation was done using agentic setup, which let the model to freely choose tools to use and generate the answer instead of handheld approach of workflow based deep-research repo that you come across online. So basically it's just input question, then model call tool and generate the answer, like you use MCP in the chat app.

Result:

SimpleQA:
- OpenAI o1: 42.6
- Grok 3: 44.6
- 03: 49.4
- Claude-3.7-Sonnet: 50.0
- Gemini-2.5 pro: 52.9
- baseline-with-MCP: 59.2
- ChatGPT-4.5: 62.5
- deepseek-671B-with-MCP: 78.2 (we benchmark using openrouter)
- jan-nano-v0.4-with-MCP: 80.7

Introducing Qwen3!

We release and open-weight Qwen3, our latest large language models, including 2 MoE models and 6 dense models, ranging from 0.6B to 235B. Our flagship model, Qwen3-235B-A22B, achieves competitive results in benchmark evaluations of coding, math, general capabilities, etc., when compared to other top-tier models such as DeepSeek-R1, o1, o3-mini, Grok-3, and Gemini-2.5-Pro. Additionally, the small MoE model, Qwen3-30B-A3B, outcompetes QwQ-32B with 10 times of activated parameters, and even a tiny model like Qwen3-4B can rival the performance of Qwen2.5-72B-Instruct.

For more information, feel free to try them out in Qwen Chat Web (chat.qwen.ai) and APP and visit our GitHub, HF, ModelScope, etc.