GRID-6X : Layout for Seamless Image Assembly
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7
Prithiv Sakthi
prithivMLmods
AI & ML interests
computer vision, realism engine, adapters
Recent Activity
updated
a Space
about 4 hours ago
prithivMLmods/Qwen2-VL-2B
upvoted
a
paper
about 4 hours ago
Bilateral Reference for High-Resolution Dichotomous Image Segmentation
updated
a Space
about 5 hours ago
prithivMLmods/FLUX-LoRA-DLC
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Organizations
prithivMLmods's activity
reacted to
Jaward's
post with ๐ฅ
1 day ago
Post
957
Implements from first-principle a discrete flow matching model for code generation- trained a small sized 2D dfm model on two variations of code for binary search. The result was amazing, code in comment:
Code: https://github.com/Jaykef/ai-algorithms/blob/main/dfm.ipynb
Code: https://github.com/Jaykef/ai-algorithms/blob/main/dfm.ipynb
- 1 reply
reacted to
suayptalha's
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2 days ago
Post
1283
๐ FastLlama Series is Live!
๐ฆพ Experience faster, lighter, and smarter language models! The new FastLlama makes Meta's LLaMA models work with smaller file sizes, lower system requirements, and higher performance. The model supports 8 languages, including English, German, and Spanish.
๐ค Built on the LLaMA 3.2-1B-Instruct model, fine-tuned with Hugging Face's SmolTalk and MetaMathQA-50k datasets, and powered by LoRA (Low-Rank Adaptation) for groundbreaking mathematical reasoning.
๐ป Its compact size makes it versatile for a wide range of applications!
๐ฌ Chat with the model:
๐ Chat Link: suayptalha/Chat-with-FastLlama
๐ Model Link: suayptalha/FastLlama-3.2-1B-Instruct
๐ฆพ Experience faster, lighter, and smarter language models! The new FastLlama makes Meta's LLaMA models work with smaller file sizes, lower system requirements, and higher performance. The model supports 8 languages, including English, German, and Spanish.
๐ค Built on the LLaMA 3.2-1B-Instruct model, fine-tuned with Hugging Face's SmolTalk and MetaMathQA-50k datasets, and powered by LoRA (Low-Rank Adaptation) for groundbreaking mathematical reasoning.
๐ป Its compact size makes it versatile for a wide range of applications!
๐ฌ Chat with the model:
๐ Chat Link: suayptalha/Chat-with-FastLlama
๐ Model Link: suayptalha/FastLlama-3.2-1B-Instruct
reacted to
m-ric's
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2 days ago
Post
1404
After 6 years, BERT, the workhorse of encoder models, finally gets a replacement: ๐ช๐ฒ๐น๐ฐ๐ผ๐บ๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐ฟ๐ป๐๐๐ฅ๐ง! ๐ค
We talk a lot about โจGenerative AIโจ, meaning "Decoder version of the Transformers architecture", but this is only one of the ways to build LLMs: encoder models, that turn a sentence in a vector, are maybe even more widely used in industry than generative models.
The workhorse for this category has been BERT since its release in 2018 (that's prehistory for LLMs).
It's not a fancy 100B parameters supermodel (just a few hundred millions), but it's an excellent workhorse, kind of a Honda Civic for LLMs.
Many applications use BERT-family models - the top models in this category cumulate millions of downloads on the Hub.
โก๏ธ Now a collaboration between Answer.AI and LightOn just introduced BERT's replacement: ModernBERT.
๐ง๐;๐๐ฅ:
๐๏ธ Architecture changes:
โ First, standard modernizations:
- Rotary positional embeddings (RoPE)
- Replace GeLU with GeGLU,
- Use Flash Attention 2
โจ The team also introduced innovative techniques like alternating attention instead of full attention, and sequence packing to get rid of padding overhead.
๐ฅ As a result, the model tops the game of encoder models:
It beats previous standard DeBERTaV3 for 1/5th the memory footprint, and runs 4x faster!
Read the blog post ๐ https://huggingface.co/blog/modernbert
We talk a lot about โจGenerative AIโจ, meaning "Decoder version of the Transformers architecture", but this is only one of the ways to build LLMs: encoder models, that turn a sentence in a vector, are maybe even more widely used in industry than generative models.
The workhorse for this category has been BERT since its release in 2018 (that's prehistory for LLMs).
It's not a fancy 100B parameters supermodel (just a few hundred millions), but it's an excellent workhorse, kind of a Honda Civic for LLMs.
Many applications use BERT-family models - the top models in this category cumulate millions of downloads on the Hub.
โก๏ธ Now a collaboration between Answer.AI and LightOn just introduced BERT's replacement: ModernBERT.
๐ง๐;๐๐ฅ:
๐๏ธ Architecture changes:
โ First, standard modernizations:
- Rotary positional embeddings (RoPE)
- Replace GeLU with GeGLU,
- Use Flash Attention 2
โจ The team also introduced innovative techniques like alternating attention instead of full attention, and sequence packing to get rid of padding overhead.
๐ฅ As a result, the model tops the game of encoder models:
It beats previous standard DeBERTaV3 for 1/5th the memory footprint, and runs 4x faster!
Read the blog post ๐ https://huggingface.co/blog/modernbert
- 1 reply
reacted to
anton-l's
post with ๐
3 days ago
Post
1800
Introducing ๐๐
๐ข๐ง๐๐๐๐ญ๐ก: the best public math pre-training dataset with 50B+ tokens!
HuggingFaceTB/finemath
Math remains challenging for LLMs and by training on FineMath we see considerable gains over other math datasets, especially on GSM8K and MATH.
We build the dataset by:
๐ ๏ธ carefully extracting math data from Common Crawl;
๐ iteratively filtering and recalling high quality math pages using a classifier trained on synthetic annotations to identify math reasoning and deduction.
We conducted a series of ablations comparing the performance of Llama-3.2-3B-Base after continued pre-training on FineMath and observe notable gains compared to the baseline model and other public math datasets.
We hope this helps advance the performance of LLMs on math and reasoning! ๐
Weโre also releasing all the ablation models as well as the evaluation code.
HuggingFaceTB/finemath-6763fb8f71b6439b653482c2
HuggingFaceTB/finemath
Math remains challenging for LLMs and by training on FineMath we see considerable gains over other math datasets, especially on GSM8K and MATH.
We build the dataset by:
๐ ๏ธ carefully extracting math data from Common Crawl;
๐ iteratively filtering and recalling high quality math pages using a classifier trained on synthetic annotations to identify math reasoning and deduction.
We conducted a series of ablations comparing the performance of Llama-3.2-3B-Base after continued pre-training on FineMath and observe notable gains compared to the baseline model and other public math datasets.
We hope this helps advance the performance of LLMs on math and reasoning! ๐
Weโre also releasing all the ablation models as well as the evaluation code.
HuggingFaceTB/finemath-6763fb8f71b6439b653482c2
posted
an
update
3 days ago
Post
1421
Qwen2VL Models: Vision and Language Processing ๐
๐FT; [ Latex OCR, Math Parsing, Text Analogy OCRTest ]
โ๏ธDemo : prithivMLmods/Qwen2-VL-2B . The demo includes the Qwen2VL 2B Base Model.
๐ฏThe space handles documenting content from the input image along with standardized plain text. It includes adjustment tools with over 30 font styles, file formatting support for PDF and DOCX, textual alignments, font size adjustments, and line spacing modifications.
๐PDFs are rendered using the ReportLab software library toolkit.
๐งตModels :
+ prithivMLmods/Qwen2-VL-OCR-2B-Instruct
+ prithivMLmods/Qwen2-VL-Ocrtest-2B-Instruct
+ prithivMLmods/Qwen2-VL-Math-Prase-2B-Instruct
๐Sample Document :
+ https://drive.google.com/file/d/1Hfqqzq4Xc-3eTjbz-jcQY84V5E1YM71E/view?usp=sharing
๐ฆCollection :
+ prithivMLmods/vision-language-models-67639f790e806e1f9799979f
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@prithivMLmods ๐ค
๐FT; [ Latex OCR, Math Parsing, Text Analogy OCRTest ]
โ๏ธDemo : prithivMLmods/Qwen2-VL-2B . The demo includes the Qwen2VL 2B Base Model.
๐ฏThe space handles documenting content from the input image along with standardized plain text. It includes adjustment tools with over 30 font styles, file formatting support for PDF and DOCX, textual alignments, font size adjustments, and line spacing modifications.
๐PDFs are rendered using the ReportLab software library toolkit.
๐งตModels :
+ prithivMLmods/Qwen2-VL-OCR-2B-Instruct
+ prithivMLmods/Qwen2-VL-Ocrtest-2B-Instruct
+ prithivMLmods/Qwen2-VL-Math-Prase-2B-Instruct
๐Sample Document :
+ https://drive.google.com/file/d/1Hfqqzq4Xc-3eTjbz-jcQY84V5E1YM71E/view?usp=sharing
๐ฆCollection :
+ prithivMLmods/vision-language-models-67639f790e806e1f9799979f
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@prithivMLmods ๐ค
- 1 reply
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m-ric's
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3 days ago
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1840
๐๐ฎ๐ ๐ ๐ข๐ง๐ ๐
๐๐๐ ๐ซ๐๐ฅ๐๐๐ฌ๐๐ฌ ๐๐ข๐๐จ๐ญ๐ซ๐จ๐ง, ๐ ๐ฆ๐ข๐๐ซ๐จ๐ฌ๐๐จ๐ฉ๐ข๐ ๐ฅ๐ข๐ ๐ญ๐ก๐๐ญ ๐ฌ๐จ๐ฅ๐ฏ๐๐ฌ ๐๐๐ ๐ญ๐ซ๐๐ข๐ง๐ข๐ง๐ ๐๐ ๐ฉ๐๐ซ๐๐ฅ๐ฅ๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง ๐ฅณ
๐ฐ๏ธ Llama-3.1-405B took 39 million GPU-hours to train, i.e. about 4.5 thousand years.
๐ด๐ป If they had needed all this time, we would have GPU stories from the time of Pharaoh ๐: "Alas, Lord of Two Lands, the shipment of counting-stones arriving from Cathay was lost to pirates, this shall delay the building of your computing temple by many moons "
๐ ๏ธ But instead, they just parallelized the training on 24k H100s, which made it take just a few months.
This required parallelizing across 4 dimensions: data, tensor, context, pipeline.
And it is infamously hard to do, making for bloated code repos that hold together only by magic.
๐ค ๐๐๐ ๐ป๐ผ๐ ๐๐ฒ ๐ฑ๐ผ๐ป'๐ ๐ป๐ฒ๐ฒ๐ฑ ๐ต๐๐ด๐ฒ ๐ฟ๐ฒ๐ฝ๐ผ๐ ๐ฎ๐ป๐๐บ๐ผ๐ฟ๐ฒ! Instead of building mega-training codes, Hugging Face colleagues cooked in the other direction, towards tiny 4D parallelism libs. A team has built Nanotron, already widely used in industry.
And now a team releases Picotron, a radical approach to code 4D Parallelism in just a few hundred lines of code, a real engineering prowess, making it much easier to understand what's actually happening!
โก ๐๐'๐ ๐๐ถ๐ป๐, ๐๐ฒ๐ ๐ฝ๐ผ๐๐ฒ๐ฟ๐ณ๐๐น:
Counting in MFU (Model FLOPs Utilization, how much the model actually uses all the compute potential), this lib reaches ~50% on SmolLM-1.7B model with 8 H100 GPUs, which is really close to what huge libs would reach. (Caution: the team is leading further benchmarks to verify this)
Go take a look ๐ https://github.com/huggingface/picotron/tree/main/picotron
๐ฐ๏ธ Llama-3.1-405B took 39 million GPU-hours to train, i.e. about 4.5 thousand years.
๐ด๐ป If they had needed all this time, we would have GPU stories from the time of Pharaoh ๐: "Alas, Lord of Two Lands, the shipment of counting-stones arriving from Cathay was lost to pirates, this shall delay the building of your computing temple by many moons "
๐ ๏ธ But instead, they just parallelized the training on 24k H100s, which made it take just a few months.
This required parallelizing across 4 dimensions: data, tensor, context, pipeline.
And it is infamously hard to do, making for bloated code repos that hold together only by magic.
๐ค ๐๐๐ ๐ป๐ผ๐ ๐๐ฒ ๐ฑ๐ผ๐ป'๐ ๐ป๐ฒ๐ฒ๐ฑ ๐ต๐๐ด๐ฒ ๐ฟ๐ฒ๐ฝ๐ผ๐ ๐ฎ๐ป๐๐บ๐ผ๐ฟ๐ฒ! Instead of building mega-training codes, Hugging Face colleagues cooked in the other direction, towards tiny 4D parallelism libs. A team has built Nanotron, already widely used in industry.
And now a team releases Picotron, a radical approach to code 4D Parallelism in just a few hundred lines of code, a real engineering prowess, making it much easier to understand what's actually happening!
โก ๐๐'๐ ๐๐ถ๐ป๐, ๐๐ฒ๐ ๐ฝ๐ผ๐๐ฒ๐ฟ๐ณ๐๐น:
Counting in MFU (Model FLOPs Utilization, how much the model actually uses all the compute potential), this lib reaches ~50% on SmolLM-1.7B model with 8 H100 GPUs, which is really close to what huge libs would reach. (Caution: the team is leading further benchmarks to verify this)
Go take a look ๐ https://github.com/huggingface/picotron/tree/main/picotron
- 1 reply
reacted to
burtenshaw's
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3 days ago
Post
2424
People are flexing their end of year stats, so I made this app to show hub stats in a tidy design!
Thanks @Ameeeee and @jfcalvo for the feature from Argilla!
burtenshaw/recap
Thanks @Ameeeee and @jfcalvo for the feature from Argilla!
burtenshaw/recap
- 1 reply
reacted to
sayakpaul's
post with ๐ค
3 days ago
Post
1460
In the past seven days, the Diffusers team has shipped:
1. Two new video models
2. One new image model
3. Two new quantization backends
4. Three new fine-tuning scripts
5. Multiple fixes and library QoL improvements
Coffee on me if someone can guess 1 - 4 correctly.
1. Two new video models
2. One new image model
3. Two new quantization backends
4. Three new fine-tuning scripts
5. Multiple fixes and library QoL improvements
Coffee on me if someone can guess 1 - 4 correctly.
- 1 reply
reacted to
davidberenstein1957's
post with ๐ฅ
3 days ago
Post
1202
๐ Tumble down the AI rabbit hole without any technical knowledge!
Explore AI models on the Hub by a simple and quick search
Demo: davidberenstein1957/transformers-pipeline-playground
Explore AI models on the Hub by a simple and quick search
Demo: davidberenstein1957/transformers-pipeline-playground
reacted to
jbilcke-hf's
post with ๐
4 days ago
Post
1579
Doing some testing with HunyuanVideo on the Hugging Face Inference Endpoints ๐ค
prompt: "a Shiba Inu is acting as a DJ, he wears sunglasses and is mixing and scratching with vinyl discs at a Ibiza sunny sand beach party"
1280x720, 22 steps, 121 frames
There are still some things to iron out regarding speed and memory usage, right now it takes 20min on an A100 (see attached charts)
but you can check it out here:
jbilcke-hf/HunyuanVideo-for-InferenceEndpoints
There are various things I want to try like the 100% diffusers version and other models (LTX-Video..)
prompt: "a Shiba Inu is acting as a DJ, he wears sunglasses and is mixing and scratching with vinyl discs at a Ibiza sunny sand beach party"
1280x720, 22 steps, 121 frames
There are still some things to iron out regarding speed and memory usage, right now it takes 20min on an A100 (see attached charts)
but you can check it out here:
jbilcke-hf/HunyuanVideo-for-InferenceEndpoints
There are various things I want to try like the 100% diffusers version and other models (LTX-Video..)
posted
an
update
4 days ago
Post
3069
๐ Here Before - Xmas๐
โจ
๐ง๐ปโ๐Models
+ [ Xmas 2D Illustration ] : strangerzonehf/Flux-Xmas-Illustration-LoRA
+ [ Xmas 3D Art ] : strangerzonehf/Flux-Xmas-3D-LoRA
+ [ Xmas Chocolate ] : strangerzonehf/Flux-Xmas-Chocolate-LoRA
+ [ Xmas Isometric Kit ] : strangerzonehf/Flux-Xmas-Isometric-Kit-LoRA
+ [ Xmas Realpix ] : strangerzonehf/Flux-Xmas-Realpix-LoRA
+ [ Xmas Anime ] : strangerzonehf/Flux-Anime-Xmas-LoRA
โ๏ธCollections
+ [ Xmas Art ] : strangerzonehf/christmas-pack-6758b199487adafaddb68f82
+ [ Stranger Zone Collection ] : prithivMLmods/stranger-zone-collections-org-6737118adcf2cb40d66d0c7e
๐ฅถPage
+ [ Stranger Zone ] : https://huggingface.co/strangerzonehf
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@prithivMLmods ๐ค
๐ง๐ปโ๐Models
+ [ Xmas 2D Illustration ] : strangerzonehf/Flux-Xmas-Illustration-LoRA
+ [ Xmas 3D Art ] : strangerzonehf/Flux-Xmas-3D-LoRA
+ [ Xmas Chocolate ] : strangerzonehf/Flux-Xmas-Chocolate-LoRA
+ [ Xmas Isometric Kit ] : strangerzonehf/Flux-Xmas-Isometric-Kit-LoRA
+ [ Xmas Realpix ] : strangerzonehf/Flux-Xmas-Realpix-LoRA
+ [ Xmas Anime ] : strangerzonehf/Flux-Anime-Xmas-LoRA
โ๏ธCollections
+ [ Xmas Art ] : strangerzonehf/christmas-pack-6758b199487adafaddb68f82
+ [ Stranger Zone Collection ] : prithivMLmods/stranger-zone-collections-org-6737118adcf2cb40d66d0c7e
๐ฅถPage
+ [ Stranger Zone ] : https://huggingface.co/strangerzonehf
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@prithivMLmods ๐ค
reacted to
merve's
post with ๐
4 days ago
Post
2565
Apollo is a new family of open-source video language models by Meta, where 3B model outperforms most 7B models and 7B outperforms most 30B models ๐งถ
โจ the models come in 1.5B https://huggingface.co/Apollo-LMMs/Apollo-1_5B-t32, 3B https://huggingface.co/Apollo-LMMs/Apollo-3B-t32 and 7B https://huggingface.co/Apollo-LMMs/Apollo-7B-t32 with A2.0 license, based on Qwen1.5 & Qwen2
โจ the authors also release a benchmark dataset https://huggingface.co/spaces/Apollo-LMMs/ApolloBench
The paper has a lot of experiments (they trained 84 models!) about what makes the video LMs work โฏ๏ธ
Try the demo for best setup here https://huggingface.co/spaces/Apollo-LMMs/Apollo-3B
they evaluate sampling strategies, scaling laws for models and datasets, video representation and more!
> The authors find out that whatever design decision was applied to small models also scale properly when the model and dataset are scaled ๐ scaling dataset has diminishing returns for smaller models
> They evaluate frame sampling strategies, and find that FPS sampling is better than uniform sampling, and they find 8-32 tokens per frame optimal
> They also compare image encoders, they try a variation of models from shape optimized SigLIP to DINOv2
they find google/siglip-so400m-patch14-384 to be most powerful ๐ฅ
> they also compare freezing different parts of models, training all stages with some frozen parts give the best yield
They eventually release three models, where Apollo-3B outperforms most 7B models and Apollo 7B outperforms 30B models ๐ฅ
โจ the models come in 1.5B https://huggingface.co/Apollo-LMMs/Apollo-1_5B-t32, 3B https://huggingface.co/Apollo-LMMs/Apollo-3B-t32 and 7B https://huggingface.co/Apollo-LMMs/Apollo-7B-t32 with A2.0 license, based on Qwen1.5 & Qwen2
โจ the authors also release a benchmark dataset https://huggingface.co/spaces/Apollo-LMMs/ApolloBench
The paper has a lot of experiments (they trained 84 models!) about what makes the video LMs work โฏ๏ธ
Try the demo for best setup here https://huggingface.co/spaces/Apollo-LMMs/Apollo-3B
they evaluate sampling strategies, scaling laws for models and datasets, video representation and more!
> The authors find out that whatever design decision was applied to small models also scale properly when the model and dataset are scaled ๐ scaling dataset has diminishing returns for smaller models
> They evaluate frame sampling strategies, and find that FPS sampling is better than uniform sampling, and they find 8-32 tokens per frame optimal
> They also compare image encoders, they try a variation of models from shape optimized SigLIP to DINOv2
they find google/siglip-so400m-patch14-384 to be most powerful ๐ฅ
> they also compare freezing different parts of models, training all stages with some frozen parts give the best yield
They eventually release three models, where Apollo-3B outperforms most 7B models and Apollo 7B outperforms 30B models ๐ฅ
- 3 replies
reacted to
davidberenstein1957's
post with ๐ฅ
5 days ago
Post
4056
Introducing the Synthetic Data Generator, a user-friendly application that takes a no-code approach to creating custom datasets with Large Language Models (LLMs). The best part: A simple step-by-step process, making dataset creation a non-technical breeze, allowing anyone to create datasets and models in minutes and without any code.
Blog: https://huggingface.co/blog/synthetic-data-generator
Space: argilla/synthetic-data-generator
Blog: https://huggingface.co/blog/synthetic-data-generator
Space: argilla/synthetic-data-generator
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- 4 replies
reacted to
lewtun's
post with ๐
6 days ago
Post
6319
We outperform Llama 70B with Llama 3B on hard math by scaling test-time compute ๐ฅ
How? By combining step-wise reward models with tree search algorithms :)
We show that smol models can match or exceed the performance of their much larger siblings when given enough "time to think"
We're open sourcing the full recipe and sharing a detailed blog post.
In our blog post we cover:
๐ Compute-optimal scaling: How we implemented DeepMind's recipe to boost the mathematical capabilities of open models at test-time.
๐ Diverse Verifier Tree Search (DVTS): An unpublished extension we developed to the verifier-guided tree search technique. This simple yet effective method improves diversity and delivers better performance, particularly at large test-time compute budgets.
๐งญ Search and Learn: A lightweight toolkit for implementing search strategies with LLMs and built for speed with vLLM
Here's the links:
- Blog post: HuggingFaceH4/blogpost-scaling-test-time-compute
- Code: https://github.com/huggingface/search-and-learn
Enjoy!
How? By combining step-wise reward models with tree search algorithms :)
We show that smol models can match or exceed the performance of their much larger siblings when given enough "time to think"
We're open sourcing the full recipe and sharing a detailed blog post.
In our blog post we cover:
๐ Compute-optimal scaling: How we implemented DeepMind's recipe to boost the mathematical capabilities of open models at test-time.
๐ Diverse Verifier Tree Search (DVTS): An unpublished extension we developed to the verifier-guided tree search technique. This simple yet effective method improves diversity and delivers better performance, particularly at large test-time compute budgets.
๐งญ Search and Learn: A lightweight toolkit for implementing search strategies with LLMs and built for speed with vLLM
Here's the links:
- Blog post: HuggingFaceH4/blogpost-scaling-test-time-compute
- Code: https://github.com/huggingface/search-and-learn
Enjoy!
- 2 replies
reacted to
alimotahharynia's
post with ๐
8 days ago
Post
1527
Here's the space for our new article that leverages LLMs with reinforcement learning to design high-quality small molecules. Check it out at
alimotahharynia/GPT-2-Drug-Generator. You can also access the article here: https://arxiv.org/abs/2411.14157.
I would be happy to receive your feedback.
I would be happy to receive your feedback.
posted
an
update
8 days ago
reacted to
dylanebert's
post with ๐
9 days ago
Post
1784
TRELLIS is now the highest ranked open-source model in the 3D Arena Leaderboard, surpassing InstantMesh
dylanebert/3d-arena
dylanebert/3d-arena
reacted to
FranckAbgrall's
post with ๐ค
10 days ago
Post
1957
Hey!
โจ If you're using HF access tokens, we just released an overview of the permissions for fine-grained tokens by hovering over the badge on token settings page (org and user)
It will show the highest permission you've set for each entity ๐
โจ If you're using HF access tokens, we just released an overview of the permissions for fine-grained tokens by hovering over the badge on token settings page (org and user)
It will show the highest permission you've set for each entity ๐
reacted to
merve's
post with ๐ง
10 days ago
Post
1616
A complete RAG pipeline includes a reranker, which ranks the documents to find the best document ๐
Same goes for multimodal RAG, multimodal rerankers which we can integrate to multimodal RAG pipelines!
Learn how to build a complete multimodal RAG pipeline with vidore/colqwen2-v1.0 as retriever, lightonai/MonoQwen2-VL-v0.1 as reranker, Qwen/Qwen2-VL-7B-Instruct as VLM in this notebook that runs on a GPU as small as L4 ๐ฅ https://huggingface.co/learn/cookbook/multimodal_rag_using_document_retrieval_and_reranker_and_vlms
Same goes for multimodal RAG, multimodal rerankers which we can integrate to multimodal RAG pipelines!
Learn how to build a complete multimodal RAG pipeline with vidore/colqwen2-v1.0 as retriever, lightonai/MonoQwen2-VL-v0.1 as reranker, Qwen/Qwen2-VL-7B-Instruct as VLM in this notebook that runs on a GPU as small as L4 ๐ฅ https://huggingface.co/learn/cookbook/multimodal_rag_using_document_retrieval_and_reranker_and_vlms
reacted to
takarajordan's
post with โค๏ธ
11 days ago
Post
2155
I'm super excited to release my first open-source text dataset:
WorldScenario 20K is a novel dataset of 20,000 synthetically generated multi-stakeholder scenarios designed to simulate real-world decision-making processes. Each scenario explores a unique environmental, societal, or economic issue.
I used the brand new meta-llama/Llama-3.3-70B-Instruct model to generate this dataset and I put the dataset through some post processing to clean and evaluate the dataset for diversity.
I'd appreciate some feedback and thoughts on my new release! Thanks!
takarajordan/WorldScenario_20K
WorldScenario 20K is a novel dataset of 20,000 synthetically generated multi-stakeholder scenarios designed to simulate real-world decision-making processes. Each scenario explores a unique environmental, societal, or economic issue.
I used the brand new meta-llama/Llama-3.3-70B-Instruct model to generate this dataset and I put the dataset through some post processing to clean and evaluate the dataset for diversity.
I'd appreciate some feedback and thoughts on my new release! Thanks!
takarajordan/WorldScenario_20K
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