Check it out here: https://github.com/JonnaMat/huggingface-slack-app
Jonna Matthiesen
JonnaMat
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repliedto their post about 22 hours ago
🕵 Want to know the second a competitor drops a new model... in Slack? 🔔
I built a Slack integration that lets you "watch" any organization or model on the Hub.
🚀 What it does:
- Pings your team when a watched org publishes a new model/dataset.
- Tracks real-time engagement (Likes, Downloads, Followers) on your own releases.
- Automated milestones so you can stop manually checking metrics.
Built with Python, uv - we all love the dopamine hit seeing the downloads climb. 🥂 posted an update about 22 hours ago
🕵 Want to know the second a competitor drops a new model... in Slack? 🔔
I built a Slack integration that lets you "watch" any organization or model on the Hub.
🚀 What it does:
- Pings your team when a watched org publishes a new model/dataset.
- Tracks real-time engagement (Likes, Downloads, Followers) on your own releases.
- Automated milestones so you can stop manually checking metrics.
Built with Python, uv - we all love the dopamine hit seeing the downloads climb. 🥂 posted an update 3 days 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 #HuggingFaceAPIOrganizations
repliedto their post about 22 hours ago
posted an update about 22 hours ago
Post
46
🕵 Want to know the second a competitor drops a new model... in Slack? 🔔
I built a Slack integration that lets you "watch" any organization or model on the Hub.
🚀 What it does:
- Pings your team when a watched org publishes a new model/dataset.
- Tracks real-time engagement (Likes, Downloads, Followers) on your own releases.
- Automated milestones so you can stop manually checking metrics.
Built with Python, uv - we all love the dopamine hit seeing the downloads climb. 🥂
I built a Slack integration that lets you "watch" any organization or model on the Hub.
🚀 What it does:
- Pings your team when a watched org publishes a new model/dataset.
- Tracks real-time engagement (Likes, Downloads, Followers) on your own releases.
- Automated milestones so you can stop manually checking metrics.
Built with Python, uv - we all love the dopamine hit seeing the downloads climb. 🥂
- 1 reply
posted an update 3 days ago
Post
134
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
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 👍 20 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
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 21 days ago
🤓 Want to learn more about FlashHead? Check out this blog post: https://huggingface.co/blog/JonnaMat/flashhead
posted an update 21 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:
🔎 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 28 days ago
Additional Jetson Orin Nano Super benchmarks will be added continuously.
posted an update 28 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.
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):
🤓 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. 😎
Introducing
👉 embedl/Cosmos-Reason2-2B-W4A16-Edge2
Optimized for Jetson Orin Nano Super and AGX Orin
🚄 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
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:
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 0.9posted 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
Dropped edge-optimized Cosmos-Reason2 running on Jetson Orin. 🚀
Check it out: embedl/Cosmos-Reason2-2B-W4A16