The Open Arabic LLM Leaderboard 2
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3485
Falcon Mamba now available now in llama.cpp !
Check out GGUF files uploaded here: tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a
Check out GGUF files uploaded here: tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a
- 3 replies
Post
4020
FalconMamba 7B - a new model from TII (Technology Innovation Institute) is out !
- Blogpost: https://huggingface.co/blog/falconmamba
- Link to collection: tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a
- Link to playground: tiiuae/falcon-mamba-playground
- Blogpost: https://huggingface.co/blog/falconmamba
- Link to collection: tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a
- Link to playground: tiiuae/falcon-mamba-playground
wdevazelhes
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10 months ago
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2847
Do I need to make it a tradition to post here every Friday? Well, here we are again!
This week, I'm happy to share that we have two official Mistral models on the Leaderboard! 🔥 You can check them out: mistralai/Mixtral-8x22B-Instruct-v0.1 and mistralai/Mixtral-8x22B-v0.1
The most exciting thing here? mistralai/Mixtral-8x22B-Instruct-v0.1 model got a first place among pretrained models with an impressive average score of 79.15!🥇 Not far behind is the Mixtral-8x22B-v0.1, achieving second place with an average score of 74.47! Well done, Mistral AI! 👏
Check out my screenshot here or explore it yourself at the https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
The second news is that CohereForAI/c4ai-command-r-plus model in 4-bit quantization got a great average score of 70.08. Cool stuff, Cohere! 😎 (and I also have the screenshot for this, don't miss it)
The last news, which might seem small but is still significant, the Leaderboard frontpage now supports Python 3.12.1. This means we're on our way to speed up the Leaderboard's performance! 🚀
If you have any comments or suggestions, feel free to also tag me on X (Twitter), I'll try to help – [at]ailozovskaya
Have a nice weekend! ✨
This week, I'm happy to share that we have two official Mistral models on the Leaderboard! 🔥 You can check them out: mistralai/Mixtral-8x22B-Instruct-v0.1 and mistralai/Mixtral-8x22B-v0.1
The most exciting thing here? mistralai/Mixtral-8x22B-Instruct-v0.1 model got a first place among pretrained models with an impressive average score of 79.15!🥇 Not far behind is the Mixtral-8x22B-v0.1, achieving second place with an average score of 74.47! Well done, Mistral AI! 👏
Check out my screenshot here or explore it yourself at the https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
The second news is that CohereForAI/c4ai-command-r-plus model in 4-bit quantization got a great average score of 70.08. Cool stuff, Cohere! 😎 (and I also have the screenshot for this, don't miss it)
The last news, which might seem small but is still significant, the Leaderboard frontpage now supports Python 3.12.1. This means we're on our way to speed up the Leaderboard's performance! 🚀
If you have any comments or suggestions, feel free to also tag me on X (Twitter), I'll try to help – [at]ailozovskaya
Have a nice weekend! ✨
- 2 replies
Post
3031
Hey everyone! 👋
This is my first post here and I’m super excited to start with not just one, but two awesome updates! 🚀
Some of you might already know that I recently started my internship at Hugging Face. I’m grateful to be a part of the LLMs evaluation team and the Open LLM Leaderboard! 🤗
First up, we’ve got some big news: we’ve just completed the evaluations for the mistral-community/Mixtral-8x22B-v0.1, and guess what? It’s now the top-performing pretrained model on the Open LLM Leaderboard! A huge shoutout to Mistral! 🏆👏 You can see more details and check out the evaluation results right here – https://huggingface.co/datasets/open-llm-leaderboard/details_mistral-community__Mixtral-8x22B-v0.1
Next, I’m excited to share a cool new feature – you can now search for models on the Open LLM Leaderboard by their licenses! 🕵️♂️ This feature will help you find the perfect model for your projects way faster. Just type "license: MIT" as a test run!
I'd be super happy if you'd follow me here for more updates on the Leaderboard and other exciting developments. Can’t wait to share more with you soon! ✨
This is my first post here and I’m super excited to start with not just one, but two awesome updates! 🚀
Some of you might already know that I recently started my internship at Hugging Face. I’m grateful to be a part of the LLMs evaluation team and the Open LLM Leaderboard! 🤗
First up, we’ve got some big news: we’ve just completed the evaluations for the mistral-community/Mixtral-8x22B-v0.1, and guess what? It’s now the top-performing pretrained model on the Open LLM Leaderboard! A huge shoutout to Mistral! 🏆👏 You can see more details and check out the evaluation results right here – https://huggingface.co/datasets/open-llm-leaderboard/details_mistral-community__Mixtral-8x22B-v0.1
Next, I’m excited to share a cool new feature – you can now search for models on the Open LLM Leaderboard by their licenses! 🕵️♂️ This feature will help you find the perfect model for your projects way faster. Just type "license: MIT" as a test run!
I'd be super happy if you'd follow me here for more updates on the Leaderboard and other exciting developments. Can’t wait to share more with you soon! ✨
Post
Check out quantized weights from ISTA-DAS Lab directly in their organisation page: https://huggingface.co/ISTA-DASLab ! With official weights of AQLM (for 2bit quantization) & QMoE (1-bit MoE quantization)
Read more about these techniques below:
AQLM paper: Extreme Compression of Large Language Models via Additive Quantization (2401.06118)
QMoE: QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models (2310.16795)
Some useful links below:
AQLM repo: https://github.com/Vahe1994/AQLM
How to use AQLM & transformers: https://huggingface.co/docs/transformers/quantization#aqlm
How to use AQLM & PEFT: https://huggingface.co/docs/peft/developer_guides/quantization#aqlm-quantizaion
Great work from @BlackSamorez and team !
Read more about these techniques below:
AQLM paper: Extreme Compression of Large Language Models via Additive Quantization (2401.06118)
QMoE: QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models (2310.16795)
Some useful links below:
AQLM repo: https://github.com/Vahe1994/AQLM
How to use AQLM & transformers: https://huggingface.co/docs/transformers/quantization#aqlm
How to use AQLM & PEFT: https://huggingface.co/docs/peft/developer_guides/quantization#aqlm-quantizaion
Great work from @BlackSamorez and team !
Post
Try out Mixtral 2-bit on a free-tier Google Colab notebook right now!
https://colab.research.google.com/drive/1-xZmBRXT5Fm3Ghn4Mwa2KRypORXb855X?usp=sharing
AQLM method has been recently introduced on transformers main branch
The 2bit model can be found here: BlackSamorez/Mixtral-8x7b-AQLM-2Bit-1x16-hf-test-dispatch
And you can read more about the method here: https://huggingface.co/docs/transformers/main/en/quantization#aqlm
Great work @BlackSamorez and team!
https://colab.research.google.com/drive/1-xZmBRXT5Fm3Ghn4Mwa2KRypORXb855X?usp=sharing
AQLM method has been recently introduced on transformers main branch
The 2bit model can be found here: BlackSamorez/Mixtral-8x7b-AQLM-2Bit-1x16-hf-test-dispatch
And you can read more about the method here: https://huggingface.co/docs/transformers/main/en/quantization#aqlm
Great work @BlackSamorez and team!
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ybelkada
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over 1 year ago
Hamza-Alobeidli
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ybelkada
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ybelkada
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