Introducing the Open FinLLM Leaderboard
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Clémentine Fourrier
clefourrier
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reacted to
fdaudens's
post with ❤️
5 months ago
Post
1866
Look at that 👀
Actual benchmarks have become too easy for recent models, much like grading high school students on middle school problems makes little sense. So the team worked on a new version of the Open LLM Leaderboard with new benchmarks.
Stellar work from @clefourrier @SaylorTwift and the team!
👉 Read the blog post: open-llm-leaderboard/blog
👉 Explore the leaderboard: open-llm-leaderboard/open_llm_leaderboard
Actual benchmarks have become too easy for recent models, much like grading high school students on middle school problems makes little sense. So the team worked on a new version of the Open LLM Leaderboard with new benchmarks.
Stellar work from @clefourrier @SaylorTwift and the team!
👉 Read the blog post: open-llm-leaderboard/blog
👉 Explore the leaderboard: open-llm-leaderboard/open_llm_leaderboard
- 1 reply
reacted to
alvdansen's
post with 👍
5 months ago
Post
6776
I had a backlog of LoRA model weights for SDXL that I decided to prioritize this weekend and publish. I know many are using SD3 right now, however if you have the time to try them, I hope you enjoy them.
I intend to start writing more fully on the thought process behind my approach to curating and training style and subject finetuning, beginning this next week.
Thank you for reading this post! You can find the models on my page and I'll drop a few previews here.
I intend to start writing more fully on the thought process behind my approach to curating and training style and subject finetuning, beginning this next week.
Thank you for reading this post! You can find the models on my page and I'll drop a few previews here.
- 4 replies
reacted to
fffiloni's
post with ❤️
6 months ago
Post
18614
🇫🇷
Quel impact de l’IA sur les filières du cinéma, de l’audiovisuel et du jeu vidéo?
Etude prospective à destination des professionnels
— CNC & BearingPoint | 09/04/2024
Si l’Intelligence Artificielle (IA) est utilisée de longue date dans les secteurs du cinéma, de l’audiovisuel et du jeu vidéo, les nouvelles applications de l’IA générative bousculent notre vision de ce dont est capable une machine et possèdent un potentiel de transformation inédit. Elles impressionnent par la qualité de leurs productions et suscitent par conséquent de nombreux débats, entre attentes et appréhensions.
Le CNC a donc décider de lancer un nouvel Observatoire de l’IA Afin de mieux comprendre les usages de l’IA et ses impacts réels sur la filière de l’image. Dans le cadre de cet Observatoire, le CNC a souhaité dresser un premier état des lieux à travers la cartographie des usages actuels ou potentiels de l’IA à chaque étape du processus de création et de diffusion d’une œuvre, en identifiant les opportunités et risques associés, notamment en termes de métiers et d’emploi. Cette étude CNC / Bearing Point en a présenté les principaux enseignements le 6 mars, lors de la journée CNC « Créer, produire, diffuser à l’heure de l’intelligence artificielle ».
Le CNC publie la version augmentée de la cartographie des usages de l’IA dans les filières du cinéma, de l’audiovisuel et du jeu vidéo.
Lien vers la cartographie complète: https://www.cnc.fr/documents/36995/2097582/Cartographie+des+usages+IA_rapport+complet.pdf/96532829-747e-b85e-c74b-af313072cab7?t=1712309387891
Quel impact de l’IA sur les filières du cinéma, de l’audiovisuel et du jeu vidéo?
Etude prospective à destination des professionnels
— CNC & BearingPoint | 09/04/2024
Si l’Intelligence Artificielle (IA) est utilisée de longue date dans les secteurs du cinéma, de l’audiovisuel et du jeu vidéo, les nouvelles applications de l’IA générative bousculent notre vision de ce dont est capable une machine et possèdent un potentiel de transformation inédit. Elles impressionnent par la qualité de leurs productions et suscitent par conséquent de nombreux débats, entre attentes et appréhensions.
Le CNC a donc décider de lancer un nouvel Observatoire de l’IA Afin de mieux comprendre les usages de l’IA et ses impacts réels sur la filière de l’image. Dans le cadre de cet Observatoire, le CNC a souhaité dresser un premier état des lieux à travers la cartographie des usages actuels ou potentiels de l’IA à chaque étape du processus de création et de diffusion d’une œuvre, en identifiant les opportunités et risques associés, notamment en termes de métiers et d’emploi. Cette étude CNC / Bearing Point en a présenté les principaux enseignements le 6 mars, lors de la journée CNC « Créer, produire, diffuser à l’heure de l’intelligence artificielle ».
Le CNC publie la version augmentée de la cartographie des usages de l’IA dans les filières du cinéma, de l’audiovisuel et du jeu vidéo.
Lien vers la cartographie complète: https://www.cnc.fr/documents/36995/2097582/Cartographie+des+usages+IA_rapport+complet.pdf/96532829-747e-b85e-c74b-af313072cab7?t=1712309387891
- 4 replies
reacted to
alielfilali01's
post with 🔥
6 months ago
Post
1458
Just passed the 25 models milestone on the
OALL/Open-Arabic-LLM-Leaderboard 🥳
And now meta-llama/Meta-Llama-3-70B-Instruct is the new hero of the leaderboard beating CohereForAI/c4ai-command-r-v01 by 5.43 points 🔥
Almost another 80 models are still PENDING ! So this might change very fast in the upcoming days
And now meta-llama/Meta-Llama-3-70B-Instruct is the new hero of the leaderboard beating CohereForAI/c4ai-command-r-v01 by 5.43 points 🔥
Almost another 80 models are still PENDING ! So this might change very fast in the upcoming days
reacted to
giux78's
post with ❤️
6 months ago
Post
1447
@FinancialSupport
and I just released a new version of the Italian LLMs leaderboard https://huggingface.co./spaces/FinancialSupport/open_ita_llm_leaderboard
using the super useful https://huggingface.co./demo-leaderboard template from @clefourrier .
We’ve evaluated over 50 models (base, merged, fine-tuned, etc.) from:
- Major companies like Meta, Mistral, Google ...
- University groups such as https://huggingface.co./sapienzanlp or https://huggingface.co./swap-uniba
- Italian Companies like https://huggingface.co./MoxoffSpA , https://huggingface.co./FairMind or https://huggingface.co./raicrits
- Various communities and individuals
All models were tested on #Italian benchmarks #mmlu #arc-c #hellaswag, which we contributed to the opensource lm-evaluation-harness library from https://huggingface.co./EleutherAI.
Plus, you can now submit your model for automatic evaluation, thanks to to https://huggingface.co./seeweb sponsored computation.
Curious about the top Italian models? Check out the leaderboard and submit your model!
https://huggingface.co./spaces/FinancialSupport/open_ita_llm_leaderboard
using the super useful https://huggingface.co./demo-leaderboard template from @clefourrier .
We’ve evaluated over 50 models (base, merged, fine-tuned, etc.) from:
- Major companies like Meta, Mistral, Google ...
- University groups such as https://huggingface.co./sapienzanlp or https://huggingface.co./swap-uniba
- Italian Companies like https://huggingface.co./MoxoffSpA , https://huggingface.co./FairMind or https://huggingface.co./raicrits
- Various communities and individuals
All models were tested on #Italian benchmarks #mmlu #arc-c #hellaswag, which we contributed to the opensource lm-evaluation-harness library from https://huggingface.co./EleutherAI.
Plus, you can now submit your model for automatic evaluation, thanks to to https://huggingface.co./seeweb sponsored computation.
Curious about the top Italian models? Check out the leaderboard and submit your model!
https://huggingface.co./spaces/FinancialSupport/open_ita_llm_leaderboard
- 1 reply
reacted to
singhsidhukuldeep's
post with ❤️
6 months ago
Post
1318
🎉 A new LLM is launched! 🚀
After checking if it's open-source or not, 🤔
you rush to see the benchmarks... 🏃♂️💨
Which benchmark does everyone check first? 🔍
MMLU (Massive Multitask Language Understanding)? 📚
Benchmarks like MMLU reaching saturation... most of the time the performance does not translate to real-world use cases! 🌐❗
Meet MMLU-Pro, released by TIGER-Lab on @huggingface ! 🐯🌍
🧪 12,217 questions across biology, business, chemistry, computer science, economics, engineering, health, history, law, mathematics, philosophy, physics, and psychology carefully validated by humans 🧑🔬
🔟 Goes to 10 options per question instead of 4, this increase in options will make the evaluation more realistic and reduce random guessing 🎯
📊 56% of questions come from MMLU, 34% from STEM websites, and the rest from TheoremQA and SciBench 📈
🤖 LLMs with weak chain-of-thought reasoning tend to perform lower, indicating it is more challenging and representative of real-world expectations 🧠💡
Any guess who tops it and who bombs it? 🤔📉📈
GPT-4o drops by 17% (from 0.887 to 0.7149) 📉
Llama-3-70B drops by 27% (from 0.820 to 0.5541) 📉
🔗 TIGER-Lab/MMLU-Pro
After checking if it's open-source or not, 🤔
you rush to see the benchmarks... 🏃♂️💨
Which benchmark does everyone check first? 🔍
MMLU (Massive Multitask Language Understanding)? 📚
Benchmarks like MMLU reaching saturation... most of the time the performance does not translate to real-world use cases! 🌐❗
Meet MMLU-Pro, released by TIGER-Lab on @huggingface ! 🐯🌍
🧪 12,217 questions across biology, business, chemistry, computer science, economics, engineering, health, history, law, mathematics, philosophy, physics, and psychology carefully validated by humans 🧑🔬
🔟 Goes to 10 options per question instead of 4, this increase in options will make the evaluation more realistic and reduce random guessing 🎯
📊 56% of questions come from MMLU, 34% from STEM websites, and the rest from TheoremQA and SciBench 📈
🤖 LLMs with weak chain-of-thought reasoning tend to perform lower, indicating it is more challenging and representative of real-world expectations 🧠💡
Any guess who tops it and who bombs it? 🤔📉📈
GPT-4o drops by 17% (from 0.887 to 0.7149) 📉
Llama-3-70B drops by 27% (from 0.820 to 0.5541) 📉
🔗 TIGER-Lab/MMLU-Pro
- 2 replies
reacted to
georgewritescode's
post with ❤️🔥
6 months ago
Post
2302
Excited to bring our benchmarking leaderboard of >100 LLM API endpoints to HF!
Speed and price are often just as important as quality when building applications with LLMs. We bring together all the data you need to consider all three when you need to pick a model and API provider.
Coverage:
‣ Quality (Index of evals, MMLU, Chatbot Arena, HumanEval, MT-Bench)
‣ Throughput (tokens/s: median, P5, P25, P75, P95)
‣ Latency (TTFT: median, P5, P25, P75, P95)
‣ Context window
‣ OpenAI library compatibility
Link to Space: ArtificialAnalysis/LLM-Performance-Leaderboard
Blog post: https://huggingface.co./blog/leaderboard-artificial-analysis
Speed and price are often just as important as quality when building applications with LLMs. We bring together all the data you need to consider all three when you need to pick a model and API provider.
Coverage:
‣ Quality (Index of evals, MMLU, Chatbot Arena, HumanEval, MT-Bench)
‣ Throughput (tokens/s: median, P5, P25, P75, P95)
‣ Latency (TTFT: median, P5, P25, P75, P95)
‣ Context window
‣ OpenAI library compatibility
Link to Space: ArtificialAnalysis/LLM-Performance-Leaderboard
Blog post: https://huggingface.co./blog/leaderboard-artificial-analysis
reacted to
osanseviero's
post with 🔥
7 months ago
Post
9298
Diaries of Open Source. Part 15 🤗
🕵️♀️Idefics 2 is out, a multimodal open-source model with very nice capabilities
Models, demo, and datasets: HuggingFaceM4/idefics2-661d1971b7c50831dd3ce0fe
Blog: https://hf.co/blog/idefics2
💾Snowflake released snowflake-arctic-embed, a family of powerful small embedding models
Model: Snowflake/snowflake-arctic-embed-m
Blog: https://www.snowflake.com/blog/introducing-snowflake-arctic-embed-snowflakes-state-of-the-art-text-embedding-family-of-models/
✨Pile-T5, EleutherAI's T5 model trained on 2T tokens
Blog: https://blog.eleuther.ai/pile-t5/
Models: EleutherAI/pile-t5-65a76a0d0022dd270b385a66
GitHub: https://github.com/EleutherAI/improved-t5
🤖CodeQwen1.5-7B base and chat models. Models trained on 3T tokens strong benchmark results for code generation, editing and SQL
Blog post: https://qwenlm.github.io/blog/codeqwen1.5/
Demo: Qwen/CodeQwen1.5-7b-Chat-demo
Models: Qwen/CodeQwen1.5-7B and Qwen/CodeQwen1.5-7B-Chat
Misc
🦉 DocOwl1.5: Unified Stucture Learning for OCR-free Document Understanding mPLUG/DocOwl
👀Cerule - a tiny Vision LM model Tensoic/Cerule-v0.1
ChemLLM - a LLM for chemistry and molecule science ⚗️https://hf.co/AI4Chem/ChemLLM-7B-Chat-1.5-DPO
Distil Whisper Large
📝New pdf/OCR datasets with 19 samples pixparse/pdf-document-ocr-datasets-660701430b0346f97c4bc628
🔥Gretel AI high quality text-to-sql synthetic dataset gretelai/synthetic_text_to_sql
🕵️♀️Idefics 2 is out, a multimodal open-source model with very nice capabilities
Models, demo, and datasets: HuggingFaceM4/idefics2-661d1971b7c50831dd3ce0fe
Blog: https://hf.co/blog/idefics2
💾Snowflake released snowflake-arctic-embed, a family of powerful small embedding models
Model: Snowflake/snowflake-arctic-embed-m
Blog: https://www.snowflake.com/blog/introducing-snowflake-arctic-embed-snowflakes-state-of-the-art-text-embedding-family-of-models/
✨Pile-T5, EleutherAI's T5 model trained on 2T tokens
Blog: https://blog.eleuther.ai/pile-t5/
Models: EleutherAI/pile-t5-65a76a0d0022dd270b385a66
GitHub: https://github.com/EleutherAI/improved-t5
🤖CodeQwen1.5-7B base and chat models. Models trained on 3T tokens strong benchmark results for code generation, editing and SQL
Blog post: https://qwenlm.github.io/blog/codeqwen1.5/
Demo: Qwen/CodeQwen1.5-7b-Chat-demo
Models: Qwen/CodeQwen1.5-7B and Qwen/CodeQwen1.5-7B-Chat
Misc
🦉 DocOwl1.5: Unified Stucture Learning for OCR-free Document Understanding mPLUG/DocOwl
👀Cerule - a tiny Vision LM model Tensoic/Cerule-v0.1
ChemLLM - a LLM for chemistry and molecule science ⚗️https://hf.co/AI4Chem/ChemLLM-7B-Chat-1.5-DPO
Distil Whisper Large
📝New pdf/OCR datasets with 19 samples pixparse/pdf-document-ocr-datasets-660701430b0346f97c4bc628
🔥Gretel AI high quality text-to-sql synthetic dataset gretelai/synthetic_text_to_sql
- 4 replies
reacted to
isidentical's
post with ❤️
7 months ago
Post
2093
Happy to announce https://imgsys.org -- a sister project to Chatbot Arena by lmsys -- for comparing different text guided image generation models models. Try it natively on HuggingFace: https://huggingface.co./spaces/fal-ai/imgsys
- 1 reply
reacted to
abidlabs's
post with ❤️
7 months ago
Post
3606
Open Models vs. Closed APIs for Software Engineers
-----------------------------------------------------------------------
If you're an ML researcher / scientist, you probably don't need much convincing to use open models instead of closed APIs -- open models give you reproducibility and let you deeply investigate the model's behavior.
But what if you are a software engineer building products on top of LLMs? I'd argue that open models are a much better option even if you are using them as APIs. For at least 3 reasons:
1) The most obvious reason is reliability of your product. Relying on a closed API means that your product has a single point-of-failure. On the other hand, there are at least 7 different API providers that offer Llama3 70B already. As well as libraries that abstract on top of these API providers so that you can make a single request that goes to different API providers depending on availability / latency.
2) Another benefit is eventual consistency going local. If your product takes off, it will be more economical and lower latency to have a dedicated inference endpoint running on your VPC than to call external APIs. If you've started with an open-source model, you can always deploy the same model locally. You don't need to modify prompts or change any surrounding logic to get consistent behavior. Minimize your technical debt from the beginning.
3) Finally, open models give you much more flexibility. Even if you keep using APIs, you might want to tradeoff latency vs. cost, or use APIs that support batches of inputs, etc. Because different API providers have different infrastructure, you can use the API provider that makes the most sense for your product -- or you can even use multiple API providers for different users (free vs. paid) or different parts of your product (priority features vs. nice-to-haves)
-----------------------------------------------------------------------
If you're an ML researcher / scientist, you probably don't need much convincing to use open models instead of closed APIs -- open models give you reproducibility and let you deeply investigate the model's behavior.
But what if you are a software engineer building products on top of LLMs? I'd argue that open models are a much better option even if you are using them as APIs. For at least 3 reasons:
1) The most obvious reason is reliability of your product. Relying on a closed API means that your product has a single point-of-failure. On the other hand, there are at least 7 different API providers that offer Llama3 70B already. As well as libraries that abstract on top of these API providers so that you can make a single request that goes to different API providers depending on availability / latency.
2) Another benefit is eventual consistency going local. If your product takes off, it will be more economical and lower latency to have a dedicated inference endpoint running on your VPC than to call external APIs. If you've started with an open-source model, you can always deploy the same model locally. You don't need to modify prompts or change any surrounding logic to get consistent behavior. Minimize your technical debt from the beginning.
3) Finally, open models give you much more flexibility. Even if you keep using APIs, you might want to tradeoff latency vs. cost, or use APIs that support batches of inputs, etc. Because different API providers have different infrastructure, you can use the API provider that makes the most sense for your product -- or you can even use multiple API providers for different users (free vs. paid) or different parts of your product (priority features vs. nice-to-haves)
reacted to
alozowski's
post with 🔥❤️
7 months ago
Post
2436
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
posted
an
update
7 months ago
Post
5149
In a basic chatbots, errors are annoyances. In medical LLMs, errors can have life-threatening consequences 🩸
It's therefore vital to benchmark/follow advances in medical LLMs before even thinking about deployment.
This is why a small research team introduced a medical LLM leaderboard, to get reproducible and comparable results between LLMs, and allow everyone to follow advances in the field.
openlifescienceai/open_medical_llm_leaderboard
Congrats to @aaditya and @pminervini !
Learn more in the blog: https://huggingface.co./blog/leaderboard-medicalllm
It's therefore vital to benchmark/follow advances in medical LLMs before even thinking about deployment.
This is why a small research team introduced a medical LLM leaderboard, to get reproducible and comparable results between LLMs, and allow everyone to follow advances in the field.
openlifescienceai/open_medical_llm_leaderboard
Congrats to @aaditya and @pminervini !
Learn more in the blog: https://huggingface.co./blog/leaderboard-medicalllm
reacted to
sted97's
post with 🔥
7 months ago
Post
2421
📣 I'm thrilled to announce "ALERT: A Comprehensive #Benchmark for Assessing #LLMs’ Safety through #RedTeaming" 🚨
📄 Paper: https://arxiv.org/pdf/2404.08676.pdf
🗃️ Repo: https://github.com/Babelscape/ALERT
🤗 ALERT benchmark: Babelscape/ALERT
🤗 ALERT DPO data: Babelscape/ALERT_DPO
As a key design principle for ALERT, we developed a fine-grained safety risk taxonomy (Fig. 2). This taxonomy serves as the foundation for the benchmark to provide detailed insights about a model’s weaknesses and vulnerabilities as well as inform targeted safety enhancements 🛡️
For collecting our prompts, we started from the popular
Anthropic's HH-RLHF data, and used automated strategies to filter/classify prompts. We then designed templates to create new prompts (providing sufficient support for each category, cf. Fig. 3) and implemented adversarial attacks.
In our experiments, we extensively evaluated several open- and closed-source LLMs (e.g. #ChatGPT, #Llama and #Mistral), highlighting their strengths and weaknesses (Table 1).
For more details, check out our preprint: https://arxiv.org/pdf/2404.08676.pdf 🤓
Huge thanks to @felfri , @PSaiml , Kristian Kersting, @navigli , @huu-ontocord and @BoLi-aisecure (and all the organizations involved: Babelscape, Sapienza NLP, TU Darmstadt, Hessian.AI, DFKI, Ontocord.AI, UChicago and UIUC)🫂
📄 Paper: https://arxiv.org/pdf/2404.08676.pdf
🗃️ Repo: https://github.com/Babelscape/ALERT
🤗 ALERT benchmark: Babelscape/ALERT
🤗 ALERT DPO data: Babelscape/ALERT_DPO
As a key design principle for ALERT, we developed a fine-grained safety risk taxonomy (Fig. 2). This taxonomy serves as the foundation for the benchmark to provide detailed insights about a model’s weaknesses and vulnerabilities as well as inform targeted safety enhancements 🛡️
For collecting our prompts, we started from the popular
Anthropic's HH-RLHF data, and used automated strategies to filter/classify prompts. We then designed templates to create new prompts (providing sufficient support for each category, cf. Fig. 3) and implemented adversarial attacks.
In our experiments, we extensively evaluated several open- and closed-source LLMs (e.g. #ChatGPT, #Llama and #Mistral), highlighting their strengths and weaknesses (Table 1).
For more details, check out our preprint: https://arxiv.org/pdf/2404.08676.pdf 🤓
Huge thanks to @felfri , @PSaiml , Kristian Kersting, @navigli , @huu-ontocord and @BoLi-aisecure (and all the organizations involved: Babelscape, Sapienza NLP, TU Darmstadt, Hessian.AI, DFKI, Ontocord.AI, UChicago and UIUC)🫂
-
- 1 reply
posted
an
update
7 months ago
Post
4127
Contamination free code evaluations with LiveCodeBench! 🖥️
LiveCodeBench is a new leaderboard, which contains:
- complete code evaluations (on code generation, self repair, code execution, tests)
- my favorite feature: problem selection by publication date 📅
This feature means that you can get model scores averaged only on new problems out of the training data. This means... contamination free code evals! 🚀
Check it out!
Blog: https://huggingface.co./blog/leaderboard-livecodebench
Leaderboard: livecodebench/leaderboard
Congrats to @StringChaos @minimario @xu3kev @kingh0730 and @FanjiaYan for the super cool leaderboard!
LiveCodeBench is a new leaderboard, which contains:
- complete code evaluations (on code generation, self repair, code execution, tests)
- my favorite feature: problem selection by publication date 📅
This feature means that you can get model scores averaged only on new problems out of the training data. This means... contamination free code evals! 🚀
Check it out!
Blog: https://huggingface.co./blog/leaderboard-livecodebench
Leaderboard: livecodebench/leaderboard
Congrats to @StringChaos @minimario @xu3kev @kingh0730 and @FanjiaYan for the super cool leaderboard!
reacted to
thomwolf's
post with 🚀🔥
7 months ago
Post
4796
Is is time for the open-source AI robots revolution 🚀?
With @haixuantao and @Leyo we’ve been playing with a low-cost DJI robot controlled by three local open-source AI models (Whisper, Idefics2, Parler-TTS - all Apache2) and orchestrated by Dora-cs.
Links to find all the hardware/software we used in the demo:
- robot control framework – dora-rs: https://github.com/dora-rs/dora
- speech-to-text model – whisper: openai/whisper-base
- vision-text model – Idefics2: HuggingFaceM4/idefics2-8b-AWQ
- text-to-speech model – ParlerTTS mini: parler-tts/parler_tts_mini_v0.1
- robot: https://dji.com/robomaster-s1
- code gist: https://gist.github.com/haixuanTao/860e1740245dc2c8dd85b496150a9320
- Larger codebase: dora-rs/dora-idefics2
- laptop/pc: any with a recent GPU card (our has a RTX 4090)
Enjoy!
With @haixuantao and @Leyo we’ve been playing with a low-cost DJI robot controlled by three local open-source AI models (Whisper, Idefics2, Parler-TTS - all Apache2) and orchestrated by Dora-cs.
Links to find all the hardware/software we used in the demo:
- robot control framework – dora-rs: https://github.com/dora-rs/dora
- speech-to-text model – whisper: openai/whisper-base
- vision-text model – Idefics2: HuggingFaceM4/idefics2-8b-AWQ
- text-to-speech model – ParlerTTS mini: parler-tts/parler_tts_mini_v0.1
- robot: https://dji.com/robomaster-s1
- code gist: https://gist.github.com/haixuanTao/860e1740245dc2c8dd85b496150a9320
- Larger codebase: dora-rs/dora-idefics2
- laptop/pc: any with a recent GPU card (our has a RTX 4090)
Enjoy!
- 4 replies
posted
an
update
7 months ago
Post
2204
🆕 Evaluate your RL agents - who's best at Atari?🏆
The new RL leaderboard evaluates agents in 87 possible environments (from Atari 🎮 to motion control simulations🚶and more)!
When you submit your model, it's run and evaluated in real time - and the leaderboard displays small videos of the best model's run, which is super fun to watch! ✨
Kudos to @qgallouedec for creating and maintaining the leaderboard!
Let's find out which agent is the best at games! 🚀
open-rl-leaderboard/leaderboard
The new RL leaderboard evaluates agents in 87 possible environments (from Atari 🎮 to motion control simulations🚶and more)!
When you submit your model, it's run and evaluated in real time - and the leaderboard displays small videos of the best model's run, which is super fun to watch! ✨
Kudos to @qgallouedec for creating and maintaining the leaderboard!
Let's find out which agent is the best at games! 🚀
open-rl-leaderboard/leaderboard
reacted to
clem's
post with 👀
7 months ago
Post
2676
We noticed that all the open-source models and datasets from https://huggingface.co./WizardLM in their personal Hugging Face account & in the Microsoft Hugging Face organization (https://huggingface.co./microsoft) have been made private by the author, which will lead some demos to fail (these models were collectively downloaded over a hundred thousand times a month).
This is the explanation that @WizardLM communicated a few hours ago: https://huggingface.co./posts/WizardLM/329547800484476#661e0d17bca1a6038b60503e
We apologize for the inconvenience & are trying to get in touch with the author & Microsoft in order to try to find a good resolution for community members. Let us know if you have any questions!
This is the explanation that @WizardLM communicated a few hours ago: https://huggingface.co./posts/WizardLM/329547800484476#661e0d17bca1a6038b60503e
We apologize for the inconvenience & are trying to get in touch with the author & Microsoft in order to try to find a good resolution for community members. Let us know if you have any questions!
- 1 reply