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! :)
Hannes von Essen
HannesVonEssen
AI & ML interests
Deep Learning Researcher at Embedl | Creator of hfviewer.com
Recent Activity
repliedto their post 1 day ago
š£ Hugging Face Visualizer, now as Chrome extension!
https://hfviewer.com
āØ After installing, Hugging Face model pages will have an architecture visualization on the model page itself!
š Link:
https://chromewebstore.google.com/detail/hugging-face-viewer/mmadlggmpkpiockpjfepaohcllbnakej
Thanks for all the nice feedback so far! ā¤ļø posted an update 2 days ago
š£ Hugging Face Visualizer, now as Chrome extension!
https://hfviewer.com
āØ After installing, Hugging Face model pages will have an architecture visualization on the model page itself!
š Link:
https://chromewebstore.google.com/detail/hugging-face-viewer/mmadlggmpkpiockpjfepaohcllbnakej
Thanks for all the nice feedback so far! ā¤ļø updated a Space 5 days ago
embedl/READMEOrganizations
replied to their post 1 day ago
posted an update 2 days ago
Post
11178
š£ Hugging Face Visualizer, now as Chrome extension!
https://hfviewer.com
āØ After installing, Hugging Face model pages will have an architecture visualization on the model page itself!
š Link:
https://chromewebstore.google.com/detail/hugging-face-viewer/mmadlggmpkpiockpjfepaohcllbnakej
Thanks for all the nice feedback so far! ā¤ļø
https://hfviewer.com
āØ After installing, Hugging Face model pages will have an architecture visualization on the model page itself!
š Link:
https://chromewebstore.google.com/detail/hugging-face-viewer/mmadlggmpkpiockpjfepaohcllbnakej
Thanks for all the nice feedback so far! ā¤ļø
- 5 replies
updated a
Space 5 days ago
reacted to unmodeled-tyler's post with š 5 days ago
Post
4080
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!
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 10 days ago
Post
176
š£ I made a visualizer for Hugging Face models: https://hfviewer.com
āØ Simply paste a Hugging Face URL to get an interactive visualization of the architecture!
š 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! ā¤ļø
āØ Simply paste a Hugging Face URL to get an interactive visualization of the architecture!
š 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! ā¤ļø
- 1 reply
updated a
model about 1 month ago
reacted to JonnaMat's post with š„š 2 months ago
Post
1080
Qwen3.5 on-device benchmarks on the Nvidia Jetson lineup are now live š
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).
š Explore TPS, TTFT, E2E latency, and TPOT. Measured on real hardware: embedl/Edge-Inference-Benchmarks
š 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.
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).
š Explore TPS, TTFT, E2E latency, and TPOT. Measured on real hardware: embedl/Edge-Inference-Benchmarks
š 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 š„ 3 months ago
Post
1616
š¤Æ Edge-Grade Vision Reasoning. Now Practically Lossless. š¤Æ
Introducing
š embedl/Cosmos-Reason2-2B-W4A16-Edge2
Optimized for Jetson Orin Nano Super and AGX Orin
nvidia .
š Try it out on Jetson (image+video+text):
š¤ What is Edge2? Most weights ā INT4 | Activations ā FP16 | Select sensitive layers ā kept in FP16.
Edge2 preserves precision where it matters most; while keeping the model small and fast enough for edge GPUs. š
Introducing
š embedl/Cosmos-Reason2-2B-W4A16-Edge2
Optimized for Jetson Orin Nano Super and AGX Orin
š 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 2š¤ What is Edge2? Most weights ā INT4 | Activations ā FP16 | Select sensitive layers ā kept in FP16.
Edge2 preserves precision where it matters most; while keeping the model small and fast enough for edge GPUs. š
posted an update 3 months ago
Post
1369
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
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 š„ 3 months ago
Post
2567
ā” Blackwell-native Vision Reasoning at the edge ā”
Released a NVFP4A16-variant of nvidia/Cosmos-Reason2-2B:
embedl/Cosmos-Reason2-2B-NVFP4A16
š Optimized for Blackwell with minimal accuracy drop compared to its FP16 counterpart.
Thorough on-device benchmarks on AGX Thor in the modelcard. š¤ š
Try it out:
Released a NVFP4A16-variant of nvidia/Cosmos-Reason2-2B:
embedl/Cosmos-Reason2-2B-NVFP4A16
š Optimized for Blackwell with minimal accuracy drop compared to its FP16 counterpart.
Thorough on-device benchmarks on AGX Thor in the modelcard. š¤ š
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 0.9published a
model 3 months ago
updated a
dataset 3 months ago
updated a
model 3 months ago