Hannes von Essen

HannesVonEssen

https://hfviewer.com/

AI & ML interests

Deep Learning Researcher at Embedl | Creator of hfviewer.com

Recent Activity

updated a model about 10 hours ago

embedl/Cosmos-Reason2-32B-W4A16-FlashHead

updated a model about 10 hours ago

embedl/Cosmos-Reason2-8B-W4A16-FlashHead

updated a model about 10 hours ago

embedl/Cosmos-Reason2-2B-NVFP4A16

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posted an update 2 days ago

Post

4632

📣 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 7 days 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 8 days ago

Post

11558

📣 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

reacted to unmodeled-tyler's post with 😎 11 days ago

Post

4098

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

posted an update 16 days ago

Post

206

📣 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

reacted to JonnaMat's post with 🔥🚀 3 months ago

Post

1081

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

reacted to JonnaMat's post with 🔥 3 months ago

Post

1617

🤯 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 3 months ago

Post

1370

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

reacted to JonnaMat's post with 🔥 3 months ago

Post

2567

⚡ Blackwell-native Vision Reasoning at the edge ⚡

Released a NVFP4A16-variant of nvidia/Cosmos-Reason2-2B:
embedl/Cosmos-Reason2-2B-NVFP4A16

💖 Optimized for Blackwell with minimal accuracy drop compared to its FP16 counterpart.

Thorough on-device benchmarks on AGX Thor in the modelcard. 🤓 📊

Try it out:

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 \
  nvcr.io/nvidia/vllm:26.01-py3 \
  vllm serve "embedl/Cosmos-Reason2-2B-NVFP4A16" \
    --host 0.0.0.0 \
    --port 8000 \
    --tensor-parallel-size 1 \
    --max-model-len 16384 \
    --gpu-memory-utilization 0.9

Hannes von Essen

AI & ML interests

Recent Activity

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