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Jonna Matthiesen

JonnaMat

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posted an update about 24 hours ago

HuggingFace x Slack We all love watching those download numbers climb on the Hub or celebrating when we release a new model. I built a Slack app that turns those milestones into team celebrations. 🥂 👀 The "Secret" Feature: It’s not just for your own wins. You can use it to keep a pulse on what other organizations are up to. Track their new model drops or download spikes... Sometimes even before the official announcements. 🕵️‍♂️ 🔗 Check out the repo and give it a star if you like it: https://github.com/JonnaMat/huggingface-slack-app #OpenSource #InferenceAPI #SlackBot #CompetitiveIntelligence #MachineLearning #HuggingFaceAPI

liked a model 13 days ago

moonshotai/Kimi-K2.5

updated a collection 14 days ago

NVIDIA Jetson Orin Nano

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posted an update about 24 hours ago

Post

HuggingFace x Slack

We all love watching those download numbers climb on the Hub or celebrating when we release a new model. I built a Slack app that turns those milestones into team celebrations. 🥂

👀 The "Secret" Feature: It’s not just for your own wins. You can use it to keep a pulse on what other organizations are up to. Track their new model drops or download spikes... Sometimes even before the official announcements. 🕵️‍♂️

🔗 Check out the repo and give it a star if you like it: https://github.com/JonnaMat/huggingface-slack-app

#OpenSource #InferenceAPI #SlackBot #CompetitiveIntelligence #MachineLearning #HuggingFaceAPI

reactedto AINovice2005's post with 👍 18 days ago

Post

378

Pro tip: If you are finetuning any model with tensorboard logs enabled, be sure to upload them to HF Hub as event artifacts, they can be viewed instantly. 🚀

I previously remembered this done in the notus model release: argilla/notus-7b-v1

Examples:

AINovice2005/ModernBERT-base-lora-cicflow-1m-r8

AINovice2005/ModernBERT-base-lora-cicflow-1m-r4

AINovice2005/ModernBERT-base-lora-cicflow-1m-r16

cc: @davidberenstein1957

repliedto their post 18 days ago

🤓 Want to learn more about FlashHead? Check out this blog post: https://huggingface.co/blog/JonnaMat/flashhead

posted an update 19 days ago

Post

5928

🚀 FlashHead: Efficient Drop-In Replacement for the Classification Head in Language Model Inference

🔎 Check out our latest FlashHead-enabled model: embedl/Cosmos-Reason2-2B-W4A16-Edge2-FlashHead

🧩 Seamless integration with vllm:

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 \
  embedl/vllm:latest-jetson-orin-flashhead \
  vllm serve "embedl/Cosmos-Reason2-2B-W4A16-Edge2-FlashHead" \
    --max-model-len 8192 \
    --gpu-memory-utilization 0.75 \
    --max-num-seqs 2 \
    --trust-remote-code

1 reply

repliedto their post 26 days ago

Additional Jetson Orin Nano Super benchmarks will be added continuously.

posted an update 26 days ago

Post

1068

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

posted an update about 1 month ago

Post

1608

🤯 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. 😎

reactedto HProg's post with 🚀 about 1 month ago

Post

1339

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

posted an update about 1 month ago

Post

2562

⚡ 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

posted an update about 1 month ago

Post

286

👀 Multimodal VLM running on 8GB RAM

Dropped edge-optimized Cosmos-Reason2 running on Jetson Orin. 🚀

Check it out: embedl/Cosmos-Reason2-2B-W4A16

Jonna Matthiesen

AI & ML interests

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