HannesVonEssen (Hannes von Essen)

posted an update 1 day ago

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📣 HF Viewer now has a HF space! 🤗

embedl/hfviewer

Visualize any model directly on Hugging Face - now 4,727 graphs!

If you like it, feel free to give the space a heart to help it grow! ❤️

And you can reply with any feedback or feature requests here!

posted an update 28 days ago

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👀 Qwen3.8-27B is identical to Qwen3.6-27B!

Interestingly, the just released 3.8 version has exactly the same architecture - meaning all the capability gains come from training improvements!

See the diff (0 changes) here!

https://hfviewer.com/compare/qwen3.6-27b-vs-qwen3.8-27b

2 replies

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reacted to JonnaMat's post with 🔥 about 1 month ago

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🚗 The reasoning backbone quadruples from 8B to 32B , while the action expert remains at 2.3B!

👀 We took a closer look at the architectural evolution from nvidia/Alpamayo-1.5-10B to nvidia/Alpamayo2-Super .

Read the analysis here:
https://huggingface.co/blog/JonnaMat/alpamayo2-super

Our analysis explores some implications of this design choice, especially from a distillation perspective where keeping the expert compact could be key for efficient deployment. 🧠

3 replies

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posted an update about 1 month ago

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🏅 HF Viewer podium

Our top most active users:

🥇#1 @Quazim0t0
🥈#2 @IvmeLabs
🥉#3 @GODELEV

If you are an HF creator and want to try entering the podium, feel free to visit the embed page to add your graph!

https://hfviewer.com/model-card-embed

2 replies

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reacted to Nymbo's post with 🔥 about 1 month ago

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Introducing Inflect-v2, two exceptionally small, open-weight English TTS models at just 3.9M and 9.3M parameters. Both generate speech multiple times faster than real-time on CPU. Despite their size, Inflect-v2 delivers quality that is competitive with much larger lightweight TTS systems, including KittenTTS, Piper, and Supertonic-3.

CPU, CUDA, PyTorch, and ONNX are supported. Apache 2.0.

See it for yourselves:
owensong/Inflect-Micro-v2
owensong/Inflect-Nano-v2

Try the Demos:
Nymbo/Inflect-TTS (unlimited CPU usage)
owensong/Inflect-v2 (ultra-fast ZeroGPU usage)

6 replies

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posted an update about 2 months ago

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📣 Kimi K3 on HF Viewer!

Kimi K3 is live on HF Viewer, along with an in-depth analysis of the 896 individual experts!

https://hfviewer.com/moonshotai/Kimi-K3

👥 Join our discussion about it on the #hfviewer discord:

https://discord.gg/a5eEmtTTPV

posted an update about 2 months ago

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🕒 Kimi K3 is expected to be released later today, at 3 pm UTC!

🔔 Get notified when it's released and explore the graph in HF Viewer!

https://hfviewer.com/moonshotai/kimi-k3

replied to their post about 2 months ago

Thanks for the report, this is now fixed! :)

posted an update about 2 months ago

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📣 Countdown Kimi K3 with us!

Read our deep dive into the architecture of Kimi K3 and get notified on July 27 when it gets released and is viewable on hfviewer.com!

https://hfviewer.com/moonshotai/kimi-k3

posted an update about 2 months ago

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📣 Understand any Hugging Face model!

💬 You can now hover a node to get a nice animated explanation of how the operation works and which paper introduced it! https://hfviewer.com

🎞️ Also, embedded HF Viewer graphs now default to being animated! Create and customize your animation at
https://hfviewer.com/model-card-embed

👥 Finally, feel free to join our discord to influence the future direction of HF Viewer! https://discord.gg/a5eEmtTTPV

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posted an update 2 months ago

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📣 HF Viewer now supports Hugging Face login! 🤗

⚡ Generate visualizations for all your models at once!

😎 Feature your models in the community showcase and set up your own profile/org collection page on hfviewer!

✍️ Write your own model articles on hfviewer in the same novel interactive style as our "Gemma 4 family" article - with linking between the graph nodes and article text!

📊 We are also now rolling out support for tensor shapes, FLOPs and param counts per layer! :)

Thanks for all your positive feedback and suggestions! ❤️

Try the logged in experience here: https://hfviewer.com/

Here are some really cool articles already written by our users:

📑 LFM2.5-Audio: edge-first speech inference by Anna Piunova at Liquid AI
https://hfviewer.com/LiquidAI/LFM2.5-Audio-1.5B

📑 DeepSeek V4 mHC Explained by Shakti Wadekar
https://hfviewer.com/deepseek-ai/DeepSeek-V4-Pro

📑 Borealis - open recipe for training Audio LLMs by Alex Wortega
https://hfviewer.com/Vikhrmodels/Borealis-5b-it

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posted an update 4 months ago

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📣 Add architecture visualization to model card!

🌟 For all creators out there: add a model visualization to your model card to capture your audience's attention!

🖱️ When clicked, it opens an interactive view with multiple levels of granularity!

1️⃣ Paste url at https://hfviewer.com/model-card-embed
2️⃣ Paste generated code in your README.md!
3️⃣ ✨

replied to their post 4 months ago

Hi! Simply paste the link to the Hugging Face model you want to view on https://hfviewer.com, or install the extension from the post to have it automatically on all model pages you visit! :)

posted an update 4 months ago

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📣 Hugging Face Visualizer, now as Chrome extension!
https://hfviewer.com

✨ After installing, Hugging Face model pages will have an architecture visualization on the model page itself!

🔗 Link:
https://chromewebstore.google.com/detail/hugging-face-viewer/mmadlggmpkpiockpjfepaohcllbnakej

Thanks for all the nice feedback so far! ❤️

5 replies

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reacted to unmodeled-tyler's post with 😎 4 months ago

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Hey Hugging Face!

Repo: https://github.com/unmodeled-tyler/vessel-browser

I wanted to share a cool feature from my open source AI native web browser, Vessel: Persistent highlights!

You can highlight anything on the page and the context is provided to the agent. It's kind of a fun way to learn about new stuff, synthesize info, or just deepen your comprehension/understanding.

Since highlights are persistent, you can close the page, come back later - and your highlights will be exactly where you left them. I've found this particularly useful when reviewing technical blogs, model cards, etc.

Check it out!

1 reply

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posted an update 4 months ago

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📣 I made a visualizer for Hugging Face models: https://hfviewer.com

✨ Simply paste a Hugging Face URL to get an interactive visualization of the architecture!

🔗 The recent Qwen3.6-27B model as an example: https://hfviewer.com/Qwen/Qwen3.6-27B

Feel free to try it out and give me feedback on how it can be improved! ❤️

1 reply

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reacted to JonnaMat's post with 🔥🚀 6 months ago

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Qwen3.5 on-device benchmarks on the Nvidia Jetson lineup are now live 🚀

We've added the latest Qwen3.5 models (0
8B - 9B) to our on-device inference benchmarks (Nvidia Jetson Orin Nano Super, AGX Orin, AGX Thor).

👉 Explore TPS, TTFT, E2E latency, and TPOT. Measured on real hardware: embedl/Edge-Inference-Benchmarks

🌟 Stay tuned for additional benchmarks and Embedl-optimized models: Enabling models run faster and on less expensive hardware.

If you're working on edge LLM deployment, we'd love to discuss your use case.

1 reply

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reacted to JonnaMat's post with 🔥 7 months ago

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🤯 Edge-Grade Vision Reasoning. Now Practically Lossless. 🤯

Introducing
👉 embedl/Cosmos-Reason2-2B-W4A16-Edge2
Optimized for Jetson Orin Nano Super and AGX Orin

nvidia .

🚄 Try it out on Jetson (image+video+text):

docker run --rm -it \
  --network host \
  --shm-size=8g \
  --ulimit memlock=-1 \
  --ulimit stack=67108864 \
  --runtime=nvidia \
  --name=vllm-serve \
  -e HF_TOKEN=hf_*** \
  -e HF_HOME=/root/.cache/huggingface \
  ghcr.io/nvidia-ai-iot/vllm:latest-jetson-orin \
  vllm serve "embedl/Cosmos-Reason2-2B-W4A16-Edge2" \
    --max-model-len 8192 \
    --gpu-memory-utilization 0.75 \
    --max-num-seqs 2

🤓 What is Edge2? Most weights → INT4 | Activations → FP16 | Select sensitive layers → kept in FP16.
Edge2 preserves precision where it matters most; while keeping the model small and fast enough for edge GPUs. 😎

posted an update 7 months ago

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Cosmos-Reason2-2B on Nano Super

Hi! Today, me and my team is releasing a version of Cosmos-Reason2-2B that is quantized so that it fits on the NVIDIA Jetson Orin Nano Super.

We managed to find a mixed precision configuration such that it maintains virtually the same accuracy as the unquantized model while being able to run really efficiently on the Nano Super and other edge devices :)

embedl/Cosmos-Reason2-2B-W4A16-Edge2

Hannes von Essen

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