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ronantakizawa

posted an update 2 days ago

Post

5119

Thank you @clem (Co-Founder & CEO of Hugging Face) for sharing my dataset on X / Twitter!

ronantakizawa/github-top-developers

#github #dataset

2 replies

danielhanchen

posted an update 3 days ago

Post

3237

You can now run GLM-4.7, the new 355B parameter SOTA model on your local device (128GB RAM).✨

The model achieves SOTA performance on coding, agentic and chat benchmarks.

GGUF: unsloth/GLM-4.7-GGUF
Guide: https://docs.unsloth.ai/models/glm-4.7

1 reply

MonsterMMORPG

posted an update about 14 hours ago

Post

942

Qwen Image Edit 2511 Free and Open Source Crushes Qwen Image Edit 2509 and Challenges Nano Banana Pro : https://www.youtube.com/watch?v=YfuQuOk2sB0

Full tutorial link > https://www.youtube.com/watch?v=YfuQuOk2sB0

Full HF article here : https://huggingface.co/blog/MonsterMMORPG/qwen-image-edit-2511-free-and-open-source-crushes

Qwen Image Edit 2511 model just published and it is literally competing against Nano Banana Pro at image editing tasks. With native whopping 2560x2560 pixels image output capability and with only 12 steps it is next level. With our installers and specially made Quant FP8 Scaled model, you can run this amazing beast even as low as 6 GB GPUs. In this tutorial, I have compared Qwen Image Edit 2511 with previous successor model Qwen Image 2509 with 12 different unique and hard prompts and cases. Everything is step by step explained and provided.

Here check some comparison images

1 reply

dhruv3006

posted an update 2 days ago

Post

993

Hey folks 👋

We’re experimenting with a new response panel layout and would love your feedback.We’re testing a more focused experience:

- Only one response section open at a time (instead of multiple)
- The response body now takes up most of the vertical space, making it easier to read and inspect

The goal is simple: reduce clutter and keep the response as the main focus.

That said, we know many developers are comfortable with the classic layout (Postman / Bruno-style), where multiple sections can stay open at once.What would you prefer?

- A new, focused single-section layout
- The classic multi-section layout
- A toggle that lets you choose between both?

Download Voiden here :https://voiden.md/download

inoculatemedia

posted an update 2 days ago

Post

1024

I’m opening the waitlist for what I believe to be the most advanced multimodal bridge for A/V professionals. Txt2img, img2video, editing, export to ProRes, apply Luts, Pexels and TouchDesigner integrations, music and voice gen, multichannel mixing.

Announcing: Lilikoi by Haawke AI

Teaser video made entirely with Lilikoi:
https://youtu.be/-O7DH7vFkYg?si=q2t5t6WjQCk2Cp0w

Https://Lilikoi.haawke.com

Technical brief:
https://haawke.com/technical_brief.html

dhruv3006

posted an update 3 days ago

Post

2682

OpenAPI specs are a great way to describe APIs in a clear, standard format. They provide a full overview of endpoints, methods, parameters etc. which makes working with APIs easier and more consistent.

Voiden lets you turn your OpenAPI spec into organized, ready-to-use API request files.

Just import your OpenAPI file, and you can immediately browse your endpoints, grouped by tags, and start testing without any manual setup.

The generated requests come pre-configured but fully editable, so you can customize them as you want.

If you want to get started with your existing APIs or try out new ones, this can save you quite some time.

Read the docs here : https://docs.voiden.md/docs/getting-started-section/getting-started/openapi-imports/

sergiopaniego

posted an update 4 days ago

Post

1766

The Christmas holidays are here! 🎄
Thinking about learning something new in AI?

@huggingface offers 12 FREE courses covering all the relevant topics, for every level of experience. A great challenge for the holidays (and worth saving for later 🙄)

Let’s explore them!

🧠 𝗟𝗟𝗠 𝗖𝗼𝘂𝗿𝘀𝗲: large language models with HF tools
https://huggingface.co/learn/llm-course

🤖 𝗔𝗴𝗲𝗻𝘁𝘀 𝗖𝗼𝘂𝗿𝘀𝗲: build and deploy AI agents
https://huggingface.co/learn/agents-course

🎨 𝗗𝗶𝗳𝗳𝘂𝘀𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲: diffusion models with 🤗 Diffusers
https://huggingface.co/learn/diffusion-course

🔊 𝗔𝘂𝗱𝗶𝗼 𝗖𝗼𝘂𝗿𝘀𝗲: transformers for audio tasks
https://huggingface.co/learn/audio-course

🎮 𝗗𝗲𝗲𝗽 𝗥𝗟 𝗖𝗼𝘂𝗿𝘀𝗲: deep reinforcement learning
https://huggingface.co/learn/deep-rl-course

👁️ 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲: modern computer vision with HF
https://huggingface.co/learn/computer-vision-course

🦾 𝗥𝗼𝗯𝗼𝘁𝗶𝗰𝘀 𝗖𝗼𝘂𝗿𝘀𝗲 (𝗟𝗲𝗥𝗼𝗯𝗼𝘁): learning-based robotics
https://huggingface.co/learn/robotics-course

🧩 𝗠𝗖𝗣 𝗖𝗼𝘂𝗿𝘀𝗲: Model Context Protocol explained
https://huggingface.co/learn/mcp-course

🧪 𝗔 𝗦𝗺𝗼𝗹 𝗖𝗼𝘂𝗿𝘀𝗲: post-training AI models
https://huggingface.co/learn/a-smol-course

🕹️ 𝗠𝗟 𝗳𝗼𝗿 𝗚𝗮𝗺𝗲𝘀: AI in game development
https://huggingface.co/learn/ml-for-games-course

🧊 𝗠𝗟 𝗳𝗼𝗿 𝟯𝗗: machine learning for 3D data
https://huggingface.co/learn/ml-for-3d-course

📘 𝗢𝗽𝗲𝗻-𝗦𝗼𝘂𝗿𝗰𝗲 𝗔𝗜 𝗖𝗼𝗼𝗸𝗯𝗼𝗼𝗸: practical AI notebooks
https://huggingface.co/learn/cookbook

All of them can be found here: https://huggingface.co/learn

codelion

posted an update 32 minutes ago

Post

Introducing Dhara-70M: A diffusion language model that achieves 3.8x higher throughput than autoregressive models!

Key findings from our research on optimal architectures for small language models:

→ Depth beats width: 32 layers outperforms 12 layers at the same parameter count
→ Best-in-class factuality: 47.5% on TruthfulQA
→ 10x training efficiency using WSD (Warmup-Stable-Decay) conversion
→ Canon layers add only 0.13% parameters but improve reasoning

We trained on 1B tokens using the optimal 50-30-20 dataset mix (PDFs + filtered web + educational content), then converted to diffusion with just 100M additional tokens.

Blog: https://huggingface.co/blog/codelion/optimal-model-architecture
Model: codelion/dhara-70m

legolasyiu

posted an update about 8 hours ago

Post

We release open-weight early experimental Codeforce metatune-gpt20b, fine tuned version of OpenAI's gpt-oss-20b model, this is one of the first public release recursive self improving AI.

EpistemeAI/Codeforce-metatune-gpt20b

2 replies

Javedalam

posted an update about 12 hours ago

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Testing LFM2-2.6B-Exp on Differential Equations

I recently tested LFM2-2.6B-Exp, an experimental language model developed by Liquid AI, to see how well it handles differential equations in a practical, step-by-step setting.

LFM2-2.6B-Exp is notable for how it was trained: it is an RL-first experimental checkpoint, built without supervised fine-tuning warm-up or distillation. Reinforcement learning was applied sequentially, starting with instruction following and later expanding to knowledge and math. This makes it a particularly interesting model to evaluate beyond benchmark scores.

In hands-on testing, the model performed surprisingly well for its size on standard undergraduate-level differential equations—first-order ODEs, second-order linear equations with constant coefficients, and nonhomogeneous problems using undetermined coefficients. It followed instructions closely and produced clear, structured solution steps.

However, the model showed limitations on more subtle methods, such as Laplace transforms with time shifting and variation of parameters, where maintaining mathematical invariants matters more than following a familiar template. In these cases, answers often looked correct structurally but failed under careful verification. This behavior is consistent with an RL-first training approach: strong at producing expected answer forms, but not always robust on deeper theoretical details.

Liquid AI, the company behind this model, is strongly focused on edge AI, developing efficient models designed for deployment outside large data-center environments. Their model lineup spans from very small models (millions of paramet

Model google notebook can be accessed here

https://colab.research.google.com/drive/1QH9d97oc68VJd0xe4vAbvHArQxpk4Ism?usp=sharing

Full detailed article here

https://fate-stingray-0b3.notion.site/Hands-On-with-LFM2-2-6B-Exp-Testing-an-RL-First-Model-on-Differential-Equations-2d43b975deec809e8b05c52652cfb500

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