ds4sd/docling-models
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Nick Brooks
nickandbro
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liked
a model
5 days ago
ds4sd/docling-models
new activity
25 days ago
google/Gemma-Embeddings-v1.0:Compatible with vLLM?
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nickandbro's activity
liked
a
model
5 days ago
What is an actorder group and what are the advantages of running this in vLLM?
4
#1 opened 10 days ago
by
nickandbro
Compatible with vLLM?
#4 opened 25 days ago
by
nickandbro
liked
a
model
about 1 month ago
Will this work with vLLM?
4
#10 opened about 2 months ago
by
nickandbro
Can you please add Nemotron 70B static?
3
#1 opened 3 months ago
by
nickandbro
Congrats to the Nvidia team!
#10 opened 3 months ago
by
nickandbro
vLLM compatible?
3
#10 opened 4 months ago
by
nickandbro
reacted to
davidberenstein1957's
post with π
3 months ago
Post
2501
Don't use an LLM when you can use a much cheaper model.
The problem is that no one tells you how to actually do it.
Just picking a pre-trained model (e.g., BERT) and throwing it at your problem won't work!
If you want a small model to perform well on your problem, you need to fine-tune it.
And to fine-tune it, you need data.
The good news is that you don't need a lot of data but instead high-quality data for your specific problem.
In the latest livestream, I showed you guys how to get started with Argilla on the Hub! Hope to see you at the next one.
https://www.youtube.com/watch?v=BEe7shiG3rY
The problem is that no one tells you how to actually do it.
Just picking a pre-trained model (e.g., BERT) and throwing it at your problem won't work!
If you want a small model to perform well on your problem, you need to fine-tune it.
And to fine-tune it, you need data.
The good news is that you don't need a lot of data but instead high-quality data for your specific problem.
In the latest livestream, I showed you guys how to get started with Argilla on the Hub! Hope to see you at the next one.
https://www.youtube.com/watch?v=BEe7shiG3rY
liked
a
model
3 months ago
reacted to
zamal's
post with π€
3 months ago
Post
1949
π New Model Release: zamal/Molmo-7B-GPTQ-4bit π
Hello lovely community,
zamal/Molmo-7B-GPTQ-4bit model is now available for all! This model has been highly quantized, reducing its size by almost six times. It now occupies significantly less space and vRAM, making it perfect for deployment on resource-constrained devices without compromising performance.
Now we get:
Efficient Performance: Maintains high accuracy while being highly quantized.
Reduced Size: The model size is reduced by nearly six times, optimizing storage and memory usage.
Versatile Application: Ideal for integrating a powerful visual language model into various projects particularly multi rag chains.
Check it out!
Hello lovely community,
zamal/Molmo-7B-GPTQ-4bit model is now available for all! This model has been highly quantized, reducing its size by almost six times. It now occupies significantly less space and vRAM, making it perfect for deployment on resource-constrained devices without compromising performance.
Now we get:
Efficient Performance: Maintains high accuracy while being highly quantized.
Reduced Size: The model size is reduced by nearly six times, optimizing storage and memory usage.
Versatile Application: Ideal for integrating a powerful visual language model into various projects particularly multi rag chains.
Check it out!
- 1 reply
Does this work with vLLM?
#9 opened 4 months ago
by
nickandbro
reacted to
MonsterMMORPG's
post with β€οΈ
4 months ago
Post
4189
Trained Myself With 256 Images on FLUX β 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 wonβt 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 wonβt 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
...
Is this compatible with the KV_Cache_dtype being FP8?
2
#1 opened 6 months ago
by
nickandbro
liked
a
model
6 months ago
Where can I find the code?
3
#832 opened 6 months ago
by
nickandbro
reacted to
tomaarsen's
post with π₯
8 months ago
Post
2394
NuMind has just released 3 new state-of-the-art GLiNER models for Named Entity Recognition/Information Extraction. These GLiNER models allow you to specify any label that you want, and it'll find spans in the text corresponding to your label. It's been shown to work quite well on unusual domains, e.g. celestial entities in my picture.
There are 3 models released:
- numind/NuNER_Zero:
The primary model, SOTA & can detect really long entities.
- numind/NuNER_Zero-span:
Slightly better performance than NuNER Zero, but can't detect entities longer than 12 tokens.
- numind/NuNER_Zero-4k:
Slightly worse than NuNER Zero, but has a context length of 4k tokens.
Some more details about these models in general:
- They are *really* small, orders of magnitude smaller than LLMs, which don't reach this level of performance.
- Because they're small - they're fast: <1s per sentence on free GPUs.
- They have an MIT license: free commercial usage.
Try out the demo here: https://huggingface.co./spaces/numind/NuZero
Or check out all of the models here: numind/nunerzero-zero-shot-ner-662b59803b9b438ff56e49e2
If there's ever a need for me to extract some information from any text: I'll be using these. Great work @Serega6678 !
There are 3 models released:
- numind/NuNER_Zero:
The primary model, SOTA & can detect really long entities.
- numind/NuNER_Zero-span:
Slightly better performance than NuNER Zero, but can't detect entities longer than 12 tokens.
- numind/NuNER_Zero-4k:
Slightly worse than NuNER Zero, but has a context length of 4k tokens.
Some more details about these models in general:
- They are *really* small, orders of magnitude smaller than LLMs, which don't reach this level of performance.
- Because they're small - they're fast: <1s per sentence on free GPUs.
- They have an MIT license: free commercial usage.
Try out the demo here: https://huggingface.co./spaces/numind/NuZero
Or check out all of the models here: numind/nunerzero-zero-shot-ner-662b59803b9b438ff56e49e2
If there's ever a need for me to extract some information from any text: I'll be using these. Great work @Serega6678 !
- 3 replies
liked
a
Space
10 months ago
reacted to
mvaloatto's
post with β€οΈ
11 months ago
Post
Want more βgood machine learningβ in your X feed? Here is a new Space for you:
π Top HF Users To Follow On X - https://huggingface.co./spaces/mvaloatto/HF2X
Ever since I fell down the AI rabbit hole, it hasnβt been super easy to spot and follow the most impactful Hugging Face contributors on X. So, inspired by @Weyaxi leaderboards, I decided to create a list just for this purpose.
Why, you ask?
First, itβs quite surprising how so many talented AI pioneers and independent contributors on X don't get the visibility/reach you might expect. Sad but true: follower count doesn't always match up with the value or innovation an individual brings to the table (just stating the obvious here).
Open source AI, in particular, thrives not just on innovation but also on the collective spirit of its believers and builders. With Hugging Face standing out as a prime hub for top AI engineers and contributors, compiling a directory of X profiles from influential figures on this platform felt like a natural step.
This Space aims to not only connect these top contributors but also guide open AI enthusiasts and newcomers towards the field's leading lights.
I put this modest page together using some web scraping and what I remember from my web dev class ages ago! Suggestions/likes are welcome - Iβm hoping to keep tweaking/upgrading it, especially if you all find it useful.
Now, letβs follow each other! Itβs time to accelerate the dissemination of our ideas, encourage collaboration within our community, and ensure that open AI developments receive the attention and recognition they deserve. π₯
π Top HF Users To Follow On X - https://huggingface.co./spaces/mvaloatto/HF2X
Ever since I fell down the AI rabbit hole, it hasnβt been super easy to spot and follow the most impactful Hugging Face contributors on X. So, inspired by @Weyaxi leaderboards, I decided to create a list just for this purpose.
Why, you ask?
First, itβs quite surprising how so many talented AI pioneers and independent contributors on X don't get the visibility/reach you might expect. Sad but true: follower count doesn't always match up with the value or innovation an individual brings to the table (just stating the obvious here).
Open source AI, in particular, thrives not just on innovation but also on the collective spirit of its believers and builders. With Hugging Face standing out as a prime hub for top AI engineers and contributors, compiling a directory of X profiles from influential figures on this platform felt like a natural step.
This Space aims to not only connect these top contributors but also guide open AI enthusiasts and newcomers towards the field's leading lights.
I put this modest page together using some web scraping and what I remember from my web dev class ages ago! Suggestions/likes are welcome - Iβm hoping to keep tweaking/upgrading it, especially if you all find it useful.
Now, letβs follow each other! Itβs time to accelerate the dissemination of our ideas, encourage collaboration within our community, and ensure that open AI developments receive the attention and recognition they deserve. π₯
- 9 replies
reacted to
fblgit's
post with π
11 months ago
Post
Senku-70B stills undefeated within EQ-Bench, latest updates from the author shows even a further increase in performance, reaching a new score of 85.09
This new mark outperform some GPT-4 models, closing further the very thin gap between OpenCommunity LLM and Closed source models.
ShinojiResearch/Senku-70B-Full
This new mark outperform some GPT-4 models, closing further the very thin gap between OpenCommunity LLM and Closed source models.
ShinojiResearch/Senku-70B-Full
- 1 reply