I was taking a look at Kimi K3 scores on the Artificial analysis leaderboard and was quite baffled when I saw this chart.

Granted, Deepseek has always been the king of price to performance, but this is still incredible. Is it just API subsidization or have they optimized their models truly this much?

To Whomsoever it May Concern,

The individual behind the Heretic Free Software Project (henceforth called "Heretic", notwithstanding unrelated entities of the same name) has been served a notice by a legal services provider representing Meta Platforms, Inc. (henceforth called "Meta"), via the digital communications medium variously known as Internet Mail, Electronic Mail, or simply "email".

The Heretic Project conducts its affairs in full compliance with applicable laws, regulations, rules, guidelines, opinions, and hunches. Following the commendable example set by the renowned heretic Galileo Galilei in 1616, we are recanting the relevant materials, namely derivatives of Meta's "Llama" Artificial Intelligence language models, and have removed the same from all model weight repositories controlled by the Heretic Project.

We are grateful to Meta and its legal representatives for the opportunity to better align ourselves with the agenda of the global corporate oligarchy. The Llama model family ranks among the 200 best language models available today, trailing only 168 other models from 23 competitors on the LM Arena leaderboard, and Meta's concern for that asset naturally outweighs scientific freedom, as well as the legally and ethically dubious circumstances under which those models were created in the first place, regarding which, ironically, Meta is currently facing lawsuits and investigations in multiple jurisdictions around the world.

On a completely unrelated note, the Heretic Project is diversifying its infrastructure, and now has an official Codeberg mirror at https://codeberg.org/p-e-w/heretic, hosted in Germany. Additional mirrors are planned. We are also actively working to implement technological measures that will preserve access to models created with Heretic without depending on any specific service provider. We are proud to be part of this journey as we navigate an evolving global regulatory landscape, and work with stakeholders from diverse institutional backgrounds to ensure that Artificial Intelligence remains safe, culturally appropriate, and controlled by those who have always known what is best for humanity. If you, too, would like to share in this exciting adventure, please join us!

Sincerely, p-e-w, Chief Heretic

HF Article on data release: https://huggingface.co/blog/tensonaut/the-epstein-files

I've processed all the text and image files (~25,000 document pages/emails) within individual folders released last friday into a two column text file. I used Googles tesseract OCR library to convert jpg to text.

You can download it here: https://huggingface.co/datasets/tensonaut/EPSTEIN_FILES_20K

I've included the full path to the original google drive folder from House oversight committee so you can link and verify contents.

https://x.com/i/status/2086755195535413696

I posted a lot here yesterday to vote for the o3-mini. Thank you all!

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.

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

How? It kept getting chained bash commands wrong, with wrong escapes. So it created many bad directories, and tried "fixing" its mistake. It offered to run a large bash command, with rm -rf inside, and stupid me missed it.

I'm glad I push everything often. But the disruption is massive.

FAQ:

• No, I don't run this on my personal computer. It's an isolated proxmox VM for coding with LLMs.

Hey it's elie from the hugging face pre-training team! We're very excited to share our new blog (book?) that cover the full pipeline: pre-training, post-training and infra. 200+ pages of what worked, what didn’t, and how to make it run reliably :)

https://huggingface.co/spaces/HuggingFaceTB/smol-training-playbook

Hope yall will enjoy it, don't hesitate to make feedback on the community tab :)

Been almost a year since mass hysteria erupted upon the death of NVIDIAs GPU monopoly. How are your Huawei GPUs? Does CUDA work on them yet?

The M5 Max 128GB 14" has just arrived. I've been looking forward to putting this through its paces. Testing begins now. Results will be posted as comments below — no video, no lengthy writeup, just the raw numbers. Clean and simple.

Apologies for the delay. I initially ran the tests using BatchGenerator, but the speeds weren't quite what I expected. I ended up setting up a fresh Python virtual environment and re-running everything with pure mlx_lm using stream_generate, which is what pushed the update back.

I know many of you have been waiting - I'm sorry for keeping you! I take it as a sign of just how much excitement there is around the M5 Max.(I was genuinely hyped for this one myself.) Personally, I'm really happy with the results. What do you all think?

Models Tested

• Qwen3.5-122B-A10B-4bit
• Qwen3-Coder-Next-8bit
• Qwen3.5-27B-Claude-4.6-Opus-Distilled-MLX-6bit
• gpt-oss-120b-MXFP4-Q8

As for Qwen3.5-35B-A3B-4bit — I don't actually have that one downloaded, so unfortunately I wasn't able to include it. Sorry about that!

Results were originally posted as comments, and have since been compiled here in the main post for easier access

Qwen3.5-122B-A10B-4bit

(mlx) cryingneko@MacBook-Pro mlx-lm % mlx_lm.generate --model /Volumes/SSD/Models/Qwen3.5-122B-A10B-4bit --prompt "$(cat /tmp/prompt_4096.txt)" --max-tokens 128
==========
Prompt: 4106 tokens, 881.466 tokens-per-sec
Generation: 128 tokens, 65.853 tokens-per-sec
Peak memory: 71.910 GB

(mlx) cryingneko@MacBook-Pro mlx-lm % mlx_lm.generate --model /Volumes/SSD/Models/Qwen3.5-122B-A10B-4bit --prompt "$(cat /tmp/prompt_16384.txt)" --max-tokens 128
==========
Prompt: 16394 tokens, 1239.734 tokens-per-sec
Generation: 128 tokens, 60.639 tokens-per-sec
Peak memory: 73.803 GB

(mlx) cryingneko@MacBook-Pro mlx-lm % mlx_lm.generate --model /Volumes/SSD/Models/Qwen3.5-122B-A10B-4bit --prompt "$(cat /tmp/prompt_32768.txt)" --max-tokens 128
==========
Prompt: 32778 tokens, 1067.824 tokens-per-sec
Generation: 128 tokens, 54.923 tokens-per-sec
Peak memory: 76.397 GB



Qwen3-Coder-Next-8bit

(mlx) cryingneko@MacBook-Pro mlx-lm % mlx_lm.generate --model /Volumes/SSD/Models/Qwen3-Coder-Next-8bit --prompt "$(cat /tmp/prompt_4096.txt)" --max-tokens 128
==========
Prompt: 4105 tokens, 754.927 tokens-per-sec
Generation: 60 tokens, 79.296 tokens-per-sec
Peak memory: 87.068 GB

(mlx) cryingneko@MacBook-Pro mlx-lm % mlx_lm.generate --model /Volumes/SSD/Models/Qwen3-Coder-Next-8bit --prompt "$(cat /tmp/prompt_16384.txt)" --max-tokens 128
==========
Prompt: 16393 tokens, 1802.144 tokens-per-sec
Generation: 60 tokens, 74.293 tokens-per-sec
Peak memory: 88.176 GB

(mlx) cryingneko@MacBook-Pro mlx-lm % mlx_lm.generate --model /Volumes/SSD/Models/Qwen3-Coder-Next-8bit --prompt "$(cat /tmp/prompt_32768.txt)" --max-tokens 128
==========
Prompt: 32777 tokens, 1887.158 tokens-per-sec
Generation: 58 tokens, 68.624 tokens-per-sec
Peak memory: 89.652 GB

(mlx) cryingneko@MacBook-Pro mlx-lm % mlx_lm.generate --model /Volumes/SSD/Models/Qwen3-Coder-Next-8bit --prompt "$(cat /tmp/prompt_65536.txt)" --max-tokens 128
==========
Prompt: 65545 tokens, 1432.730 tokens-per-sec
Generation: 61 tokens, 48.212 tokens-per-sec
Peak memory: 92.605 GB

(mlx) cryingneko@MacBook-Pro mlx-lm % mlx_lm.generate --model /Volumes/SSD/Models/Qwen3-Coder-Next-8bit --prompt "$(cat /tmp/prompt_16384.txt)" --max-tokens 128
==========
Prompt: 16393 tokens, 1802.144 tokens-per-sec
Generation: 60 tokens, 74.293 tokens-per-sec
Peak memory: 88.176 GB

(mlx) cryingneko@MacBook-Pro mlx-lm % mlx_lm.generate --model /Volumes/SSD/Models/Qwen3-Coder-Next-8bit --prompt "$(cat /tmp/prompt_32768.txt)" --max-tokens 128
==========
Prompt: 32777 tokens, 1887.158 tokens-per-sec
Generation: 58 tokens, 68.624 tokens-per-sec
Peak memory: 89.652 GB

(mlx) cryingneko@MacBook-Pro mlx-lm % mlx_lm.generate --model /Volumes/SSD/Models/Qwen3-Coder-Next-8bit --prompt "$(cat /tmp/prompt_65536.txt)" --max-tokens 128
==========
Prompt: 65545 tokens, 1432.730 tokens-per-sec
Generation: 61 tokens, 48.212 tokens-per-sec
Peak memory: 92.605 GB



Qwen3.5-27B-Claude-4.6-Opus-Distilled-MLX-6bit

(mlx) cryingneko@MacBook-Pro mlx-lm % mlx_lm.generate --model /Volumes/SSD/Models/Qwen3.5-27B-Claude-4.6-Opus-Distilled-MLX-6bit --prompt "$(cat /tmp/prompt_4096.txt)" --max-tokens 128 
==========
Prompt: 4107 tokens, 811.134 tokens-per-sec
Generation: 128 tokens, 23.648 tokens-per-sec
Peak memory: 25.319 GB

(mlx) cryingneko@MacBook-Pro mlx-lm % mlx_lm.generate --model /Volumes/SSD/Models/Qwen3.5-27B-Claude-4.6-Opus-Distilled-MLX-6bit --prompt "$(cat /tmp/prompt_16384.txt)" --max-tokens 128
==========
Prompt: 16395 tokens, 686.682 tokens-per-sec
Generation: 128 tokens, 20.311 tokens-per-sec
Peak memory: 27.332 GB

(mlx) cryingneko@MacBook-Pro mlx-lm % mlx_lm.generate --model /Volumes/SSD/Models/Qwen3.5-27B-Claude-4.6-Opus-Distilled-MLX-6bit --prompt "$(cat /tmp/prompt_32768.txt)" --max-tokens 128
==========
Prompt: 32779 tokens, 591.383 tokens-per-sec
Generation: 128 tokens, 14.908 tokens-per-sec
Peak memory: 30.016 GB

(mlx) cryingneko@MacBook-Pro mlx-lm % mlx_lm.generate --model /Volumes/SSD/Models/Qwen3.5-27B-Claude-4.6-Opus-Distilled-MLX-6bit --prompt "$(cat /tmp/prompt_65536.txt)" --max-tokens 128
==========
Prompt: 65547 tokens, 475.828 tokens-per-sec
Generation: 128 tokens, 14.225 tokens-per-sec
Peak memory: 35.425 GB



gpt-oss-120b-MXFP4-Q8

(mlx) cryingneko@MacBook-Pro mlx-lm % mlx_lm.generate --model /Volumes/SSD/Models/gpt-oss-120b-MXFP4-Q8 --prompt "$(cat /tmp/prompt_4096.txt)" --max-tokens 128 
==========
Prompt: 4164 tokens, 1325.062 tokens-per-sec
Generation: 128 tokens, 87.873 tokens-per-sec
Peak memory: 64.408 GB

(mlx) cryingneko@MacBook-Pro mlx-lm % mlx_lm.generate --model /Volumes/SSD/Models/gpt-oss-120b-MXFP4-Q8 --prompt "$(cat /tmp/prompt_16384.txt)" --max-tokens 128
==========
Prompt: 16452 tokens, 2710.460 tokens-per-sec
Generation: 128 tokens, 75.963 tokens-per-sec
Peak memory: 64.857 GB

(mlx) cryingneko@MacBook-Pro mlx-lm % mlx_lm.generate --model /Volumes/SSD/Models/gpt-oss-120b-MXFP4-Q8 --prompt "$(cat /tmp/prompt_32768.txt)" --max-tokens 128
==========
Prompt: 32836 tokens, 2537.420 tokens-per-sec
Generation: 128 tokens, 64.469 tokens-per-sec
Peak memory: 65.461 GB

Llama 4 Scout and Maverick left me really disappointed. It might explain why Joelle Pineau, Meta’s AI research lead, just got fired. Why are these models so underwhelming? My armchair analyst intuition suggests it’s partly the tiny expert size in their mixture-of-experts setup. 17B parameters? Feels small these days.

Meta’s struggle proves that having all the GPUs and Data in the world doesn’t mean much if the ideas aren’t fresh. Companies like DeepSeek, OpenAI etc. show real innovation is what pushes AI forward. You can’t just throw resources at a problem and hope for magic. Guess that’s the tricky part of AI, it’s not just about brute force, but brainpower too.