Open ASR Leaderboard: Towards Reproducible and Transparent Multilingual and Long-Form Speech Recognition Evaluation
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6229
Excited to onboard FeatherlessAI on Hugging Face as an Inference Provider - they bring a fleet of 6,700+ LLMs on-demand on the Hugging Face Hub 🤯
Starting today, you'd be able to access all those LLMs (OpenAI compatible) on HF model pages and via OpenAI client libraries too! 💥
Go, play with it today: https://huggingface.co/blog/inference-providers-featherless
P.S. They're also bringing on more GPUs to support all your concurrent requests!
Starting today, you'd be able to access all those LLMs (OpenAI compatible) on HF model pages and via OpenAI client libraries too! 💥
Go, play with it today: https://huggingface.co/blog/inference-providers-featherless
P.S. They're also bringing on more GPUs to support all your concurrent requests!
- 1 reply
Post
4674
hey hey
@mradermacher
- VB from Hugging Face here, we'd love to onboard you over to our optimised xet backend! 💥
as you know we're in the process of upgrading our storage backend to xet (which helps us scale and offer blazingly fast upload/ download speeds too): https://huggingface.co/blog/xet-on-the-hub and now that we are certain that the backend can scale with even big models like Llama 4/ Qwen 3 - we;re moving to the next phase of inviting impactful orgs and users on the hub over as you are a big part of the open source ML community - we would love to onboard you next and create some excitement about it in the community too!
in terms of actual steps - it should be as simple as one of the org admins to join hf.co/join/xet - we'll take care of the rest.
p.s. you'd need to have a the latest hf_xet version of huggingface_hub lib but everything else should be the same: https://huggingface.co/docs/hub/storage-backends#using-xet-storage
p.p.s. this is fully backwards compatible so everything will work as it should! 🤗
as you know we're in the process of upgrading our storage backend to xet (which helps us scale and offer blazingly fast upload/ download speeds too): https://huggingface.co/blog/xet-on-the-hub and now that we are certain that the backend can scale with even big models like Llama 4/ Qwen 3 - we;re moving to the next phase of inviting impactful orgs and users on the hub over as you are a big part of the open source ML community - we would love to onboard you next and create some excitement about it in the community too!
in terms of actual steps - it should be as simple as one of the org admins to join hf.co/join/xet - we'll take care of the rest.
p.s. you'd need to have a the latest hf_xet version of huggingface_hub lib but everything else should be the same: https://huggingface.co/docs/hub/storage-backends#using-xet-storage
p.p.s. this is fully backwards compatible so everything will work as it should! 🤗
-
- 16 replies
mindhunter23
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8 months ago
reach-vb
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9 months ago
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2144
Hi community,
Few days back, I posted about my ongoing research on making reasoning mamba models and I found great insights from the community.
Today, I am announcing an update to the model weights. With newer checkpoints, the Falcon3 Mamba R1 model now outperforms very large transformer based LLMs (including Gemini) for Formal Logic questions of MMLU. It scores 60% on formal logic which is considered a tough subset of questions in MMLU.
I would highly appreciate your insights and suggestions on this new checkpoint.
Model Repo: hanzla/Falcon3-Mamba-R1-v0
Chat space: hanzla/Falcon3MambaReasoner
Few days back, I posted about my ongoing research on making reasoning mamba models and I found great insights from the community.
Today, I am announcing an update to the model weights. With newer checkpoints, the Falcon3 Mamba R1 model now outperforms very large transformer based LLMs (including Gemini) for Formal Logic questions of MMLU. It scores 60% on formal logic which is considered a tough subset of questions in MMLU.
I would highly appreciate your insights and suggestions on this new checkpoint.
Model Repo: hanzla/Falcon3-Mamba-R1-v0
Chat space: hanzla/Falcon3MambaReasoner
Post
4075
Hello community,
I want to share my work of creating a reasoning mamba model
I used GRPO over Falcon3 Mamba Instruct to make this model. It generates blazing fast response while building good logic to answer challenging questions.
Give it a try:
Model repo: hanzla/Falcon3-Mamba-R1-v0
Space: hanzla/Falcon3MambaReasoner
Looking forward to community feedback.
I want to share my work of creating a reasoning mamba model
I used GRPO over Falcon3 Mamba Instruct to make this model. It generates blazing fast response while building good logic to answer challenging questions.
Give it a try:
Model repo: hanzla/Falcon3-Mamba-R1-v0
Space: hanzla/Falcon3MambaReasoner
Looking forward to community feedback.
- 2 replies
Post
1271
Gemma 3 is a game changer for on device multimodal applications.
Try for yourself how a 4 billion parameter model can be so good.
hanzla/PlaygroundGemma3
Try for yourself how a 4 billion parameter model can be so good.
hanzla/PlaygroundGemma3
- 1 reply
mindhunter23
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10 months ago