Share and discover more about AI with social posts from the community.huggingface/OpenAi
Anybody ever play Final Fantasy: Crystal Chronicles?
Like, *really* play it?
Mag Mell has been in my head recently. What a place that was.
Those cocoons looked like I could lay down inside of one, and it would be the most powerful sleep of a lifetime, with dreams that would last one thousand years, and I'd wake up with the wisdom of generations.
...Hey, anybody like text adventures?
Like, *really* play it?
Mag Mell has been in my head recently. What a place that was.
Those cocoons looked like I could lay down inside of one, and it would be the most powerful sleep of a lifetime, with dreams that would last one thousand years, and I'd wake up with the wisdom of generations.
...Hey, anybody like text adventures?
Last Week in Medical AI: Top Research Papers/Models
๐ (September 7 - September 14, 2024)
๐ Medical AI Paper of the week
Chai-1 Foundation model molecular structure prediction
Medical LLMs & Benchmarks
- BrainWave: A Brain Signal Foundation Model
- DS-ViT: Vision Transformer for Alzheimerโs Diagnosis
- EyeCLIP: Visualโlanguage model for ophthalmic
- Segment Anything Model for Tumor Segmentation
- MEDIC: Evaluating LLMs in Clinical Applications
Medical LLM Applications
- KARGEN: Radiology Report Generation LLMs
- DrugAgent: Explainable Drug Repurposing Agents
- Improving RAG in Medicine with Follow-up Questions
Frameworks and Methodologies
- Infrastructure for Automatic Cell Segmentation
- Data Alignment for Dermatology AI
- Diagnostic Reasoning in Natural Language
- Two-Stage Instruction Fine-tuning Approach for Med
AI in Healthcare Ethics
- Concerns and Choices of Using LLMs for Healthcare
- Understanding Fairness in Recommender Systems
- Towards Fairer Health Recommendations
Check the full thread: https://x.com/OpenlifesciAI/status/1832476252260712788
Thank you for your continued support and love for this series! Stay up-to-date with weekly updates on Medical LLMs, datasets, and top research papers by following @aaditya ๐ค
๐ (September 7 - September 14, 2024)
๐ Medical AI Paper of the week
Chai-1 Foundation model molecular structure prediction
Medical LLMs & Benchmarks
- BrainWave: A Brain Signal Foundation Model
- DS-ViT: Vision Transformer for Alzheimerโs Diagnosis
- EyeCLIP: Visualโlanguage model for ophthalmic
- Segment Anything Model for Tumor Segmentation
- MEDIC: Evaluating LLMs in Clinical Applications
Medical LLM Applications
- KARGEN: Radiology Report Generation LLMs
- DrugAgent: Explainable Drug Repurposing Agents
- Improving RAG in Medicine with Follow-up Questions
Frameworks and Methodologies
- Infrastructure for Automatic Cell Segmentation
- Data Alignment for Dermatology AI
- Diagnostic Reasoning in Natural Language
- Two-Stage Instruction Fine-tuning Approach for Med
AI in Healthcare Ethics
- Concerns and Choices of Using LLMs for Healthcare
- Understanding Fairness in Recommender Systems
- Towards Fairer Health Recommendations
Check the full thread: https://x.com/OpenlifesciAI/status/1832476252260712788
Thank you for your continued support and love for this series! Stay up-to-date with weekly updates on Medical LLMs, datasets, and top research papers by following @aaditya ๐ค
Trained Myself With 256 Images on FLUX โ Results Mind Blowing
Detailed Full Workflow
Medium article : https://medium.com/@furkangozukara/ultimate-flux-lora-training-tutorial-windows-and-cloud-deployment-abb72f21cbf8
Windows main tutorial : https://youtu.be/nySGu12Y05k
Cloud tutorial for GPU poor or scaling : https://youtu.be/-uhL2nW7Ddw
Full detailed results and conclusions : https://www.patreon.com/posts/111891669
Full config files and details to train : https://www.patreon.com/posts/110879657
SUPIR Upscaling (default settings are now perfect) : https://youtu.be/OYxVEvDf284
I used my Poco X6 Camera phone and solo taken images
My dataset is far from being ready, thus I have used so many repeating and almost same images, but this was rather experimental
Hopefully I will continue taking more shots and improve dataset and reduce size in future
I trained Clip-L and T5-XXL Text Encoders as well
Since there was too much push from community that my workflow wonโt work with expressions, I had to take a break from research and use whatever I have
I used my own researched workflow for training with Kohya GUI and also my own self developed SUPIR app batch upscaling with face upscaling and auto LLaVA captioning improvement
Download images to see them in full size, the last provided grid is 50% downscaled
Workflow
Gather a dataset that has expressions and perspectives that you like after training, this is crucial, whatever you add, it can generate perfect
Follow one of the LoRA training tutorials / guides
After training your LoRA, use your favorite UI to generate images
I prefer SwarmUI and here used prompts (you can add specific expressions to prompts) including face inpainting :
https://gist.github.com/FurkanGozukara/ce72861e52806c5ea4e8b9c7f4409672
After generating images, use SUPIR to upscale 2x with maximum resemblance
Short Conclusions
Using 256 images certainly caused more overfitting than necessary
Detailed Full Workflow
Medium article : https://medium.com/@furkangozukara/ultimate-flux-lora-training-tutorial-windows-and-cloud-deployment-abb72f21cbf8
Windows main tutorial : https://youtu.be/nySGu12Y05k
Cloud tutorial for GPU poor or scaling : https://youtu.be/-uhL2nW7Ddw
Full detailed results and conclusions : https://www.patreon.com/posts/111891669
Full config files and details to train : https://www.patreon.com/posts/110879657
SUPIR Upscaling (default settings are now perfect) : https://youtu.be/OYxVEvDf284
I used my Poco X6 Camera phone and solo taken images
My dataset is far from being ready, thus I have used so many repeating and almost same images, but this was rather experimental
Hopefully I will continue taking more shots and improve dataset and reduce size in future
I trained Clip-L and T5-XXL Text Encoders as well
Since there was too much push from community that my workflow wonโt work with expressions, I had to take a break from research and use whatever I have
I used my own researched workflow for training with Kohya GUI and also my own self developed SUPIR app batch upscaling with face upscaling and auto LLaVA captioning improvement
Download images to see them in full size, the last provided grid is 50% downscaled
Workflow
Gather a dataset that has expressions and perspectives that you like after training, this is crucial, whatever you add, it can generate perfect
Follow one of the LoRA training tutorials / guides
After training your LoRA, use your favorite UI to generate images
I prefer SwarmUI and here used prompts (you can add specific expressions to prompts) including face inpainting :
https://gist.github.com/FurkanGozukara/ce72861e52806c5ea4e8b9c7f4409672
After generating images, use SUPIR to upscale 2x with maximum resemblance
Short Conclusions
Using 256 images certainly caused more overfitting than necessary
Medium
Ultimate FLUX LoRA Training Tutorial: Windows and Cloud Deployment
I have done total 104 different LoRA trainings and compared each one of them to find the very best hyper parameters and the workflow forโฆ
Researchers from Tencent have developed DepthCrafter, a novel method for generating temporally consistent long depth sequences for open-world videos using video diffusion models.
It leverages a pre-trained image-to-video diffusion model (SVD) as the foundation and uses a 3-stage training strategy on paired video-depth datasets:
1. Train on a large realistic dataset (1-25 frames)
2. Fine-tune temporal layers on realistic data (1-110 frames)
3. Fine-tune spatial layers on synthetic data (45 frames)
It adapts SVD's conditioning mechanism for frame-by-frame video input and employs latent diffusion in VAE space for efficiency.
Sprinkle some intelligent inference strategy for extremely long videos:
- Segment-wise processing (up to 110 frames)
- Noise initialization to anchor depth distributions
- Latent interpolation for seamless stitching
And outperforms SOTA methods on multiple datasets (Sintel, ScanNet, KITTI, Bonn).
Read here: https://depthcrafter.github.io
It leverages a pre-trained image-to-video diffusion model (SVD) as the foundation and uses a 3-stage training strategy on paired video-depth datasets:
1. Train on a large realistic dataset (1-25 frames)
2. Fine-tune temporal layers on realistic data (1-110 frames)
3. Fine-tune spatial layers on synthetic data (45 frames)
It adapts SVD's conditioning mechanism for frame-by-frame video input and employs latent diffusion in VAE space for efficiency.
Sprinkle some intelligent inference strategy for extremely long videos:
- Segment-wise processing (up to 110 frames)
- Noise initialization to anchor depth distributions
- Latent interpolation for seamless stitching
And outperforms SOTA methods on multiple datasets (Sintel, ScanNet, KITTI, Bonn).
Read here: https://depthcrafter.github.io
nanoGPT with Sigmoid Self-Attention
I couldnโt resist had to give it a try:)
Some observations on M2:
SSA was ~5-10% faster in training with similar final loss values, slightly less coherent text generation, marginally higher perplexity, and lower memory usage compared to softmax.
Code: https://github.com/Jaykef/ai-algorithms/blob/main/sigmoid_attn.ipynb
I couldnโt resist had to give it a try:)
Some observations on M2:
SSA was ~5-10% faster in training with similar final loss values, slightly less coherent text generation, marginally higher perplexity, and lower memory usage compared to softmax.
Code: https://github.com/Jaykef/ai-algorithms/blob/main/sigmoid_attn.ipynb
GitHub
ai-algorithms/sigmoid_attn.ipynb at main ยท Jaykef/ai-algorithms
First-principle implementations of various AI algorithms using a wide range of deep learning frameworks, accompanied by relevant research papers - Jaykef/ai-algorithms
How much VRAM will you need for training your AI model? ๐พ๐ง
Check out this app where you convert:
Pytorch/tensorflow summary -> required VRAM
or
Parameter count -> required VRAM
Use it in: http://howmuchvram.com
And everything is open source! Ask for new functionalities or contribute in:
https://github.com/AlexBodner/How_Much_VRAM
If it's useful to you leave a star ๐and share it to someone that will find the tool useful!
More discussion in: https://x.com/AlexBodner_/status/1832054850294812679
Check out this app where you convert:
Pytorch/tensorflow summary -> required VRAM
or
Parameter count -> required VRAM
Use it in: http://howmuchvram.com
And everything is open source! Ask for new functionalities or contribute in:
https://github.com/AlexBodner/How_Much_VRAM
If it's useful to you leave a star ๐and share it to someone that will find the tool useful!
More discussion in: https://x.com/AlexBodner_/status/1832054850294812679
inflatebot/MN-12B-Mag-Mell-R1
https://huggingface.co/inflatebot/MN-12B-Mag-Mell-R1
MN-12B-Mag-Mell is a multi-stage merge, inspired by hypermerges like Tiefighter and Umbral Mind, intended for use as a general-purpose "Best of Nemo" model for co-writing, roleplay, and text adventures.
Consistently, Mag Mell produced prose that shocked testers, with a minimum of "slop". It also exhibited a unique sense of humor, and a propensity for inserting bespoke details into adventuring scenarios.
https://huggingface.co/inflatebot/MN-12B-Mag-Mell-R1
MN-12B-Mag-Mell is a multi-stage merge, inspired by hypermerges like Tiefighter and Umbral Mind, intended for use as a general-purpose "Best of Nemo" model for co-writing, roleplay, and text adventures.
Consistently, Mag Mell produced prose that shocked testers, with a minimum of "slop". It also exhibited a unique sense of humor, and a propensity for inserting bespoke details into adventuring scenarios.
huggingface.co
inflatebot/MN-12B-Mag-Mell-R1 ยท Hugging Face
Weโre on a journey to advance and democratize artificial intelligence through open source and open science.
Have you tried the new SQL Console yet?
Would love to know any queries you've tried or general feedback! If you haven't go try it out and let us know ๐ค
If you have some interesting queries feel free to share the URLs as well!
Would love to know any queries you've tried or general feedback! If you haven't go try it out and let us know ๐ค
If you have some interesting queries feel free to share the URLs as well!
๐๐
๐๐ฟ๐ฎ๐ฐ๐๐ถ๐ป๐ด ๐๐ผ๐๐ฟ ๐๐ง๐ ๐ ๐๐ฒ๐ฏ๐ฝ๐ฎ๐ด๐ฒ๐ ๐๐ผ ๐บ๐ฎ๐ฟ๐ธ๐ฑ๐ผ๐๐ป ๐ถ๐ ๐ป๐ผ๐ ๐ฝ๐ผ๐๐๐ถ๐ฏ๐น๐ฒ ๐ฒ๐ป๐ฑ-๐๐ผ-๐ฒ๐ป๐ฑ ๐๐ถ๐๐ต ๐ฎ ๐๐ถ๐บ๐ฝ๐น๐ฒ ๐๐๐ ! ๐
Jina just released Reader-LM, that handles the whole pipeline of extracting markdown from HTML webpages.
A while ago, Jina had released a completely code-based deterministic program to do this extraction, based on some heuristics : e.g., โif the text is in a <p> tag, keep it, but if itโs hidden behind another, remove itโ.
๐ค But they received complaints from readers: some found it too detailed, other not enough, depending on the pages.
โก๏ธ So they decided, ๐บ๐ฎ๐๐ฏ๐ฒ ๐ต๐ฒ๐๐ฟ๐ถ๐๐๐ถ๐ฐ๐ ๐๐ฒ๐ฟ๐ฒ ๐ป๐ผ๐ ๐ฒ๐ป๐ผ๐๐ด๐ต: ๐ถ๐ป๐๐๐ฒ๐ฎ๐ฑ, ๐๐ต๐ฒ๐ ๐๐ฟ๐ถ๐ฒ๐ฑ ๐๐ผ ๐๐ฟ๐ฎ๐ถ๐ป ๐ฎ ๐๐๐ ๐๐ผ ๐ฑ๐ผ ๐๐ต๐ฒ ๐ฐ๐ผ๐บ๐ฝ๐น๐ฒ๐๐ฒ ๐ฒ๐ ๐๐ฟ๐ฎ๐ฐ๐๐ถ๐ผ๐ป. This LLM does not need to be very strong,but it should handle a very long context: itโs a challenging, โshallow-but-wideโ architecture.
๐ง๐ฒ๐ฐ๐ต๐ป๐ถ๐ฐ๐ฎ๐น ๐ถ๐ป๐๐ถ๐ด๐ต๐๐:
2๏ธโฃ models: Reader-LM-0.5B and 1.5B
โ๏ธ Two stages of training: first, short and simple HTML to get the basics, then ramp up to longer and harder HTML up to 128k tokens
๐ Use contrastive search for decoding: this empirically reduces โrepeating outputโ issues
โก๏ธ Their models beat much larger models at HTML extraction ๐ฅ
๐ค Weights available on HF (sadly cc-by-nc license):
jinaai/reader-lm-1.5b
Jina just released Reader-LM, that handles the whole pipeline of extracting markdown from HTML webpages.
A while ago, Jina had released a completely code-based deterministic program to do this extraction, based on some heuristics : e.g., โif the text is in a <p> tag, keep it, but if itโs hidden behind another, remove itโ.
๐ค But they received complaints from readers: some found it too detailed, other not enough, depending on the pages.
โก๏ธ So they decided, ๐บ๐ฎ๐๐ฏ๐ฒ ๐ต๐ฒ๐๐ฟ๐ถ๐๐๐ถ๐ฐ๐ ๐๐ฒ๐ฟ๐ฒ ๐ป๐ผ๐ ๐ฒ๐ป๐ผ๐๐ด๐ต: ๐ถ๐ป๐๐๐ฒ๐ฎ๐ฑ, ๐๐ต๐ฒ๐ ๐๐ฟ๐ถ๐ฒ๐ฑ ๐๐ผ ๐๐ฟ๐ฎ๐ถ๐ป ๐ฎ ๐๐๐ ๐๐ผ ๐ฑ๐ผ ๐๐ต๐ฒ ๐ฐ๐ผ๐บ๐ฝ๐น๐ฒ๐๐ฒ ๐ฒ๐ ๐๐ฟ๐ฎ๐ฐ๐๐ถ๐ผ๐ป. This LLM does not need to be very strong,but it should handle a very long context: itโs a challenging, โshallow-but-wideโ architecture.
๐ง๐ฒ๐ฐ๐ต๐ป๐ถ๐ฐ๐ฎ๐น ๐ถ๐ป๐๐ถ๐ด๐ต๐๐:
2๏ธโฃ models: Reader-LM-0.5B and 1.5B
โ๏ธ Two stages of training: first, short and simple HTML to get the basics, then ramp up to longer and harder HTML up to 128k tokens
๐ Use contrastive search for decoding: this empirically reduces โrepeating outputโ issues
โก๏ธ Their models beat much larger models at HTML extraction ๐ฅ
๐ค Weights available on HF (sadly cc-by-nc license):
jinaai/reader-lm-1.5b
Hugging face presents FineVideo ๐ฅ! Unlocking the next generation of Video understanding ๐
๐คฏ3400 hours of annotated Creative Common videos with rich character descriptions, scene splits, mood, and content descriptions per scene as well as QA pairs.
๐ฅ
@mfarre processed over 2M videos of Youtube-CC to make this incredibly powerful selection.
Very psyched to fine-tune idefics on this dataset. โก๏ธ
Explore the videos:
HuggingFaceFV/FineVideo-Explorer
๐คฏ3400 hours of annotated Creative Common videos with rich character descriptions, scene splits, mood, and content descriptions per scene as well as QA pairs.
๐ฅ
@mfarre processed over 2M videos of Youtube-CC to make this incredibly powerful selection.
Very psyched to fine-tune idefics on this dataset. โก๏ธ
Explore the videos:
HuggingFaceFV/FineVideo-Explorer
Hugging face presents FineVideo ๐ฅ! Unlocking the next generation of Video understanding ๐
๐คฏ3400 hours of annotated Creative Common videos with rich character descriptions, scene splits, mood, and content descriptions per scene as well as QA pairs.
๐ฅ
@mfarre processed over 2M videos of Youtube-CC to make this incredibly powerful selection.
Very psyched to fine-tune idefics on this dataset. โก๏ธ
Explore the videos:
HuggingFaceFV/FineVideo-Explorer
๐คฏ3400 hours of annotated Creative Common videos with rich character descriptions, scene splits, mood, and content descriptions per scene as well as QA pairs.
๐ฅ
@mfarre processed over 2M videos of Youtube-CC to make this incredibly powerful selection.
Very psyched to fine-tune idefics on this dataset. โก๏ธ
Explore the videos:
HuggingFaceFV/FineVideo-Explorer
๐๐ฉ๐๐ง๐๐ ๐๐ข๐ง๐๐ฅ๐ฅ๐ฒ ๐ซ๐๐ฏ๐๐๐ฅ๐ฌ โ๐โ: ๐๐ซ๐๐ณ๐ฒ ๐๐ก๐๐ข๐ง-๐จ๐-๐ญ๐ก๐จ๐ฎ๐ ๐ก๐ญ-๐ญ๐ฎ๐ง๐๐ ๐ฆ๐จ๐๐๐ฅ >> ๐๐๐-๐๐จ ๐ฅ
OpenAI had hinted at a mysterious โproject strawberryโ for a long time: ๐๐ต๐ฒ๐ ๐ฝ๐๐ฏ๐น๐ถ๐๐ต๐ฒ๐ฑ ๐๐ต๐ถ๐ ๐ป๐ฒ๐ ๐บ๐ผ๐ฑ๐ฒ๐น ๐ฐ๐ฎ๐น๐น๐ฒ๐ฑ โ๐ผ๐ญโ ๐ญ๐ต๐ผ๐๐ฟ ๐ฎ๐ด๐ผ, ๐ฎ๐ป๐ฑ ๐๐ต๐ฒ ๐ฝ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ป๐ฐ๐ฒ ๐ถ๐ ๐ท๐๐๐ ๐บ๐ถ๐ป๐ฑ-๐ฏ๐น๐ผ๐๐ถ๐ป๐ด.
๐คฏ Ranks among the top 500 students in the US in a qualifier for the USA Math Olympiad
๐คฏ Beats human-PhD-level accuracy by 8% on GPQA, hard science problems benchmark where the previous best was Claude 3.5 Sonnet with 59.4.
๐คฏ Scores 78.2% on vision benchmark MMMU, making it the first model competitive w/ human experts
๐คฏ GPT-4o on MATH scored 60% โ o1 scores 95%
How did they pull this? Sadly OpenAI keeps increasing their performance in โmaking cryptic AF reports to not reveal any real infoโ, so here are excerpts:
๐ฌ โ๐ผ๐ญ ๐๐๐ฒ๐ ๐ฎ ๐ฐ๐ต๐ฎ๐ถ๐ป ๐ผ๐ณ ๐๐ต๐ผ๐๐ด๐ต๐ ๐๐ต๐ฒ๐ป ๐ฎ๐๐๐ฒ๐บ๐ฝ๐๐ถ๐ป๐ด ๐๐ผ ๐๐ผ๐น๐๐ฒ ๐ฎ ๐ฝ๐ฟ๐ผ๐ฏ๐น๐ฒ๐บ. ๐ง๐ต๐ฟ๐ผ๐๐ด๐ต ๐ฟ๐ฒ๐ถ๐ป๐ณ๐ผ๐ฟ๐ฐ๐ฒ๐บ๐ฒ๐ป๐ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด, ๐ผ๐ญ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ ๐๐ผ ๐ต๐ผ๐ป๐ฒ ๐ถ๐๐ ๐ฐ๐ต๐ฎ๐ถ๐ป ๐ผ๐ณ ๐๐ต๐ผ๐๐ด๐ต๐ ๐ฎ๐ป๐ฑ ๐ฟ๐ฒ๐ณ๐ถ๐ป๐ฒ ๐๐ต๐ฒ ๐๐๐ฟ๐ฎ๐๐ฒ๐ด๐ถ๐ฒ๐ ๐ถ๐ ๐๐๐ฒ๐. It learns to recognize and correct its mistakes.โ
And of course, they decide to hide the content of this precious Chain-of-
Thought. Would it be for maximum profit? Of course not, you awful capitalist, itโs to protect users:
๐ฌ โWe also do not want to make an unaligned chain of thought directly visible to users.โ
Theyโre right, it would certainly have hurt my feelings to see the internal of this model tearing apart math problems.
๐ค I suspect it could be not only CoT, but also some agentic behaviour where the model can just call a code executor. The kind of score improvement the show certainly looks like the ones you see with agents.
This model will be immediately released for ChatGPT and some โtrusted API usersโ.
Letโs start cooking to release the same thing in 6 months! ๐
OpenAI had hinted at a mysterious โproject strawberryโ for a long time: ๐๐ต๐ฒ๐ ๐ฝ๐๐ฏ๐น๐ถ๐๐ต๐ฒ๐ฑ ๐๐ต๐ถ๐ ๐ป๐ฒ๐ ๐บ๐ผ๐ฑ๐ฒ๐น ๐ฐ๐ฎ๐น๐น๐ฒ๐ฑ โ๐ผ๐ญโ ๐ญ๐ต๐ผ๐๐ฟ ๐ฎ๐ด๐ผ, ๐ฎ๐ป๐ฑ ๐๐ต๐ฒ ๐ฝ๐ฒ๐ฟ๐ณ๐ผ๐ฟ๐บ๐ฎ๐ป๐ฐ๐ฒ ๐ถ๐ ๐ท๐๐๐ ๐บ๐ถ๐ป๐ฑ-๐ฏ๐น๐ผ๐๐ถ๐ป๐ด.
๐คฏ Ranks among the top 500 students in the US in a qualifier for the USA Math Olympiad
๐คฏ Beats human-PhD-level accuracy by 8% on GPQA, hard science problems benchmark where the previous best was Claude 3.5 Sonnet with 59.4.
๐คฏ Scores 78.2% on vision benchmark MMMU, making it the first model competitive w/ human experts
๐คฏ GPT-4o on MATH scored 60% โ o1 scores 95%
How did they pull this? Sadly OpenAI keeps increasing their performance in โmaking cryptic AF reports to not reveal any real infoโ, so here are excerpts:
๐ฌ โ๐ผ๐ญ ๐๐๐ฒ๐ ๐ฎ ๐ฐ๐ต๐ฎ๐ถ๐ป ๐ผ๐ณ ๐๐ต๐ผ๐๐ด๐ต๐ ๐๐ต๐ฒ๐ป ๐ฎ๐๐๐ฒ๐บ๐ฝ๐๐ถ๐ป๐ด ๐๐ผ ๐๐ผ๐น๐๐ฒ ๐ฎ ๐ฝ๐ฟ๐ผ๐ฏ๐น๐ฒ๐บ. ๐ง๐ต๐ฟ๐ผ๐๐ด๐ต ๐ฟ๐ฒ๐ถ๐ป๐ณ๐ผ๐ฟ๐ฐ๐ฒ๐บ๐ฒ๐ป๐ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด, ๐ผ๐ญ ๐น๐ฒ๐ฎ๐ฟ๐ป๐ ๐๐ผ ๐ต๐ผ๐ป๐ฒ ๐ถ๐๐ ๐ฐ๐ต๐ฎ๐ถ๐ป ๐ผ๐ณ ๐๐ต๐ผ๐๐ด๐ต๐ ๐ฎ๐ป๐ฑ ๐ฟ๐ฒ๐ณ๐ถ๐ป๐ฒ ๐๐ต๐ฒ ๐๐๐ฟ๐ฎ๐๐ฒ๐ด๐ถ๐ฒ๐ ๐ถ๐ ๐๐๐ฒ๐. It learns to recognize and correct its mistakes.โ
And of course, they decide to hide the content of this precious Chain-of-
Thought. Would it be for maximum profit? Of course not, you awful capitalist, itโs to protect users:
๐ฌ โWe also do not want to make an unaligned chain of thought directly visible to users.โ
Theyโre right, it would certainly have hurt my feelings to see the internal of this model tearing apart math problems.
๐ค I suspect it could be not only CoT, but also some agentic behaviour where the model can just call a code executor. The kind of score improvement the show certainly looks like the ones you see with agents.
This model will be immediately released for ChatGPT and some โtrusted API usersโ.
Letโs start cooking to release the same thing in 6 months! ๐
I believe Hugging Face should have something similar to Hacktoberfest. I miss the days when there were events like this every 3 months for audio, deep reinforcement learning, gradio themes, but it turns out everything slowed down. There are no more Hugging Face events.
๐ข The Three-hop (๐กaspect + ๐คopinion + ๐ง reason) Chain-of-Thought concept + LLM represent a decent concept for reasoning emotions of participants in textual dialogues.
Delighted to share the tutorial video which make you aware of:
โ The proper application of LLM towards implicit IR
โ Ways for aligning different information types (causes and states) within the same LLM
โ Launch your LLM in GoogleColab that is capable for characters Emotion Extraction in dialogues ๐งช
๐ฅ: https://www.youtube.com/watch?v=vRVDQa7vfkU
Project: https://github.com/nicolay-r/THOR-ECAC
Paper: https://aclanthology.org/2024.semeval-1.4/
Model card:
nicolay-r/flan-t5-emotion-cause-thor-base
Delighted to share the tutorial video which make you aware of:
โ The proper application of LLM towards implicit IR
โ Ways for aligning different information types (causes and states) within the same LLM
โ Launch your LLM in GoogleColab that is capable for characters Emotion Extraction in dialogues ๐งช
๐ฅ: https://www.youtube.com/watch?v=vRVDQa7vfkU
Project: https://github.com/nicolay-r/THOR-ECAC
Paper: https://aclanthology.org/2024.semeval-1.4/
Model card:
nicolay-r/flan-t5-emotion-cause-thor-base
The Romulus model series has been released on Hugging Face, continually pre-trained on 34,864,949 tokens of French laws and intended to serve as a foundation for fine-tuning on labeled data ๐ค
The training code, dataset and model weights are open and available free on HF and the training was based on H100 provided by Microsoft for Startups using Unsloth AI by @danielhanchen and @shimmyshimmer ๐ฆฅ
Link to the base model:
louisbrulenaudet/Romulus-cpt-Llama-3.1-8B-v0.1
Link to the instruct model:
louisbrulenaudet/Romulus-cpt-Llama-3.1-8B-v0.1-Instruct
Link to the dataset:
louisbrulenaudet/Romulus-cpt-fr
Please note that these models have not been aligned for the production of usable texts as they stand, and will certainly need to be refined for the desired tasks in order to produce satisfactory results.https://cdn-uploads.huggingface.co/production/uploads/6459fa0f5b3111fbe83286e1/n_KKbhGEDZg-2NMBu3OGo.jpeg
The training code, dataset and model weights are open and available free on HF and the training was based on H100 provided by Microsoft for Startups using Unsloth AI by @danielhanchen and @shimmyshimmer ๐ฆฅ
Link to the base model:
louisbrulenaudet/Romulus-cpt-Llama-3.1-8B-v0.1
Link to the instruct model:
louisbrulenaudet/Romulus-cpt-Llama-3.1-8B-v0.1-Instruct
Link to the dataset:
louisbrulenaudet/Romulus-cpt-fr
Please note that these models have not been aligned for the production of usable texts as they stand, and will certainly need to be refined for the desired tasks in order to produce satisfactory results.https://cdn-uploads.huggingface.co/production/uploads/6459fa0f5b3111fbe83286e1/n_KKbhGEDZg-2NMBu3OGo.jpeg
> ๐ช๐ฎ๐ป๐ ๐๐ผ ๐ธ๐ป๐ผ๐ ๐ต๐ผ๐ ๐บ๐๐ฐ๐ต ๐ฎ๐ป ๐๐ฃ๐ ๐๐๐ ๐ฐ๐ฎ๐น๐น ๐ฐ๐ผ๐๐๐ ๐๐ผ๐?
I've just made this Space that gets you the API price for any LLM call, for nearly all inference providers out there!
This is based on a comment by @victor under my HF Post a few months back, and leverages BerriAI's data for LLM prices.
Check it out here ๐
m-ric/text_to_dollars
I've just made this Space that gets you the API price for any LLM call, for nearly all inference providers out there!
This is based on a comment by @victor under my HF Post a few months back, and leverages BerriAI's data for LLM prices.
Check it out here ๐
m-ric/text_to_dollars
Auto-regressive LMs have ruled, but encoder-based architectures like GLiNER are proving to be just as powerful for information extraction while offering better efficiency and interpretability. ๐โจ
Past encoder backbones were limited by small pre-training datasets and old techniques, but with innovations like LLM2Vec, we've transformed decoders into high-performing encoders! ๐๐ก
Whatโs New?
๐นConverted Llama & Qwen decoders to advanced encoders
๐นImproved GLiNER architecture to be able to work with rotary positional encoding
๐นNew GLiNER (zero-shot NER) & GLiClass (zero-shot classification) models
๐ฅ Check it out:
New models:
knowledgator/llm2encoder-66d1c76e3c8270397efc5b5e
GLiNER package: https://github.com/urchade/GLiNER
GLiClass package: https://github.com/Knowledgator/GLiClass
๐ป Read our blog for more insights, and stay tuned for whatโs next!
https://medium.com/@knowledgrator/llm2encoders-e7d90b9f5966
Past encoder backbones were limited by small pre-training datasets and old techniques, but with innovations like LLM2Vec, we've transformed decoders into high-performing encoders! ๐๐ก
Whatโs New?
๐นConverted Llama & Qwen decoders to advanced encoders
๐นImproved GLiNER architecture to be able to work with rotary positional encoding
๐นNew GLiNER (zero-shot NER) & GLiClass (zero-shot classification) models
๐ฅ Check it out:
New models:
knowledgator/llm2encoder-66d1c76e3c8270397efc5b5e
GLiNER package: https://github.com/urchade/GLiNER
GLiClass package: https://github.com/Knowledgator/GLiClass
๐ป Read our blog for more insights, and stay tuned for whatโs next!
https://medium.com/@knowledgrator/llm2encoders-e7d90b9f5966
GitHub
GitHub - urchade/GLiNER: Generalist and Lightweight Model for Named Entity Recognition (Extract any entity types from texts) @โฆ
Generalist and Lightweight Model for Named Entity Recognition (Extract any entity types from texts) @ NAACL 2024 - urchade/GLiNER
Free research tip:
Get used to writing the first draft of your paper in markdown using vscodeโs jupyter notebook extension - it lets you do quick sanity checks with code and maths - an absolute AAA experience:)
Get used to writing the first draft of your paper in markdown using vscodeโs jupyter notebook extension - it lets you do quick sanity checks with code and maths - an absolute AAA experience:)
made an image similarity demo to test out the
mistral-community/pixtral-12b-240910
model .
If anyone knows how to generate captions with it , please do let me know x ๐
here's the demo :
Tonic/Pixtral
hope you like it ๐ค
mistral-community/pixtral-12b-240910
model .
If anyone knows how to generate captions with it , please do let me know x ๐
here's the demo :
Tonic/Pixtral
hope you like it ๐ค
What if we asked the AI what it thought of our hugging face profile? ๐น
I've released a new space capable of doing it.... watch out, it hits hard! ๐ฅ
Try it now โก๏ธ
enzostvs/hugger-roaster
Share your roast below ๐
I've released a new space capable of doing it.... watch out, it hits hard! ๐ฅ
Try it now โก๏ธ
enzostvs/hugger-roaster
Share your roast below ๐