video-p2p-library
AI & ML interests
None defined yet.
Recent Activity
View all activity
video-p2p-library's activity
ameerazam08
posted
an
update
12 days ago
Post
1601
R1 is out! And with a lot of other R1 releated models...
Shaldon
authored
6
papers
27 days ago
Video-P2P: Video Editing with Cross-attention Control
Paper
•
2303.04761
•
Published
•
2
Direct Inversion: Boosting Diffusion-based Editing with 3 Lines of Code
Paper
•
2310.01506
•
Published
RL-GPT: Integrating Reinforcement Learning and Code-as-policy
Paper
•
2402.19299
•
Published
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
Paper
•
2403.18814
•
Published
•
47
Multi-modal Cooking Workflow Construction for Food Recipes
Paper
•
2008.09151
•
Published
•
1
Generative Video Propagation
Paper
•
2412.19761
•
Published
ehristoforu
posted
an
update
about 2 months ago
Post
3251
✒️ Ultraset - all-in-one dataset for SFT training in Alpaca format.
fluently-sets/ultraset
❓ Ultraset is a comprehensive dataset for training Large Language Models (LLMs) using the SFT (instruction-based Fine-Tuning) method. This dataset consists of over 785 thousand entries in eight languages, including English, Russian, French, Italian, Spanish, German, Chinese, and Korean.
🤯 Ultraset solves the problem faced by users when selecting an appropriate dataset for LLM training. It combines various types of data required to enhance the model's skills in areas such as text writing and editing, mathematics, coding, biology, medicine, finance, and multilingualism.
🤗 For effective use of the dataset, it is recommended to utilize only the "instruction," "input," and "output" columns and train the model for 1-3 epochs. The dataset does not include DPO or Instruct data, making it suitable for training various types of LLM models.
❇️ Ultraset is an excellent tool to improve your language model's skills in diverse knowledge areas.
fluently-sets/ultraset
❓ Ultraset is a comprehensive dataset for training Large Language Models (LLMs) using the SFT (instruction-based Fine-Tuning) method. This dataset consists of over 785 thousand entries in eight languages, including English, Russian, French, Italian, Spanish, German, Chinese, and Korean.
🤯 Ultraset solves the problem faced by users when selecting an appropriate dataset for LLM training. It combines various types of data required to enhance the model's skills in areas such as text writing and editing, mathematics, coding, biology, medicine, finance, and multilingualism.
🤗 For effective use of the dataset, it is recommended to utilize only the "instruction," "input," and "output" columns and train the model for 1-3 epochs. The dataset does not include DPO or Instruct data, making it suitable for training various types of LLM models.
❇️ Ultraset is an excellent tool to improve your language model's skills in diverse knowledge areas.
Post
9166
Google drops Gemini 2.0 Flash Thinking
a new experimental model that unlocks stronger reasoning capabilities and shows its thoughts. The model plans (with thoughts visible), can solve complex problems with Flash speeds, and more
now available in anychat, try it out: akhaliq/anychat
a new experimental model that unlocks stronger reasoning capabilities and shows its thoughts. The model plans (with thoughts visible), can solve complex problems with Flash speeds, and more
now available in anychat, try it out: akhaliq/anychat
- 2 replies
Post
10041
QwQ-32B-Preview is now available in anychat
A reasoning model that is competitive with OpenAI o1-mini and o1-preview
try it out: akhaliq/anychat
A reasoning model that is competitive with OpenAI o1-mini and o1-preview
try it out: akhaliq/anychat
- 1 reply
Post
3974
Post
2926
anychat
supports chatgpt, gemini, perplexity, claude, meta llama, grok all in one app
try it out there: akhaliq/anychat
supports chatgpt, gemini, perplexity, claude, meta llama, grok all in one app
try it out there: akhaliq/anychat
JoseRFJunior
posted
an
update
6 months ago
Post
1694
JoseRFJunior/TransNAR
https://github.com/JoseRFJuniorLLMs/TransNAR
https://arxiv.org/html/2406.09308v1
TransNAR hybrid architecture. Similar to Alayrac et al, we interleave existing Transformer layers with gated cross-attention layers which enable information to flow from the NAR to the Transformer. We generate queries from tokens while we obtain keys and values from nodes and edges of the graph. The node and edge embeddings are obtained by running the NAR on the graph version of the reasoning task to be solved. When experimenting with pre-trained Transformers, we initially close the cross-attention gate, in order to fully preserve the language model’s internal knowledge at the beginning of training.
https://github.com/JoseRFJuniorLLMs/TransNAR
https://arxiv.org/html/2406.09308v1
TransNAR hybrid architecture. Similar to Alayrac et al, we interleave existing Transformer layers with gated cross-attention layers which enable information to flow from the NAR to the Transformer. We generate queries from tokens while we obtain keys and values from nodes and edges of the graph. The node and edge embeddings are obtained by running the NAR on the graph version of the reasoning task to be solved. When experimenting with pre-trained Transformers, we initially close the cross-attention gate, in order to fully preserve the language model’s internal knowledge at the beginning of training.
ehristoforu
posted
an
update
7 months ago
Post
4388
😏 Hello from Project Fluently Team!
✨ Finally we can give you some details about Supple Diffusion. We worked on it for a long time and we have little left, we apologize that we had to increase the work time.
🛠️ Some technical information. The first version will be the Small version (there will also be Medium, Large, Huge, possibly Tiny), it will be based on the SD1 architecture, that is, one text encoder, U-net, VAE. Now about each component, the first is a text encoder, it will be a CLIP model (perhaps not CLIP-L-path14), CLIP was specially retrained by us in order to achieve the universality of the model in understanding completely different styles and to simplify the prompt as much as possible. Next, we did U-net, U-net in a rather complicated way, first we trained different parts (types) of data with different U-nets, then we carried out merging using different methods, then we trained DPO and SPO using methods, and then we looked at the remaining shortcomings and further trained model, details will come later. We left VAE the same as in SD1 architecture.
🙌 Compatibility. Another goal of the Supple model series is full compatibility with Auto1111 and ComfyUI already at the release stage, the model is fully supported by these interfaces and the diffusers library and does not require adaptation, your usual Sampling methods are also compatible, such as DPM++ 2M Karras, DPM++ SDE and others.
🧐 Today, without demo images (there wasn’t much time), final work is underway on the model and we are already preparing to develop the Medium version, the release of the Small version will most likely be in mid-August or earlier.
😻 Feel free to ask your questions in the comments below the post, we will be happy to answer them, have a nice day!
✨ Finally we can give you some details about Supple Diffusion. We worked on it for a long time and we have little left, we apologize that we had to increase the work time.
🛠️ Some technical information. The first version will be the Small version (there will also be Medium, Large, Huge, possibly Tiny), it will be based on the SD1 architecture, that is, one text encoder, U-net, VAE. Now about each component, the first is a text encoder, it will be a CLIP model (perhaps not CLIP-L-path14), CLIP was specially retrained by us in order to achieve the universality of the model in understanding completely different styles and to simplify the prompt as much as possible. Next, we did U-net, U-net in a rather complicated way, first we trained different parts (types) of data with different U-nets, then we carried out merging using different methods, then we trained DPO and SPO using methods, and then we looked at the remaining shortcomings and further trained model, details will come later. We left VAE the same as in SD1 architecture.
🙌 Compatibility. Another goal of the Supple model series is full compatibility with Auto1111 and ComfyUI already at the release stage, the model is fully supported by these interfaces and the diffusers library and does not require adaptation, your usual Sampling methods are also compatible, such as DPM++ 2M Karras, DPM++ SDE and others.
🧐 Today, without demo images (there wasn’t much time), final work is underway on the model and we are already preparing to develop the Medium version, the release of the Small version will most likely be in mid-August or earlier.
😻 Feel free to ask your questions in the comments below the post, we will be happy to answer them, have a nice day!
- 1 reply
Post
3507
Use GPT-4, GPT-4 Turbo Preview, GPT-3.5 Turbo, BingAI, and other models. The interface is similar to ChatGPT, with a speedy API endpoint.
https://huggingface.co/spaces/NiansuhAI/Copilot
https://huggingface.co/spaces/NiansuhAI/Copilot
- 4 replies
ehristoforu
posted
an
update
8 months ago
Post
6369
🤗 Hello from the Project Fluently team!
🥏 We are ready to announce a new series of Supple Diffusion models, these are new generation diffusion models (about 1-2 weeks left before release).
🦾 The new series aims to take diffusion models to the next level, with performance and versatility as the main goal.
🧐 How will our models be better than others? Firstly, we worked on the CLIP models, now they understand your requests better, it will become easier to process. Secondly, we trained the models with high quality, even better than all our previous ones. Thirdly, you won’t have to keep 20 models on your disk; only 4-6 will be enough.
🗺️ Roadmap:
1. Create Supple Diffusion Small
2. Creating Supple Diffusion Medium
3. Create Supple Diffusion Large
🎆 Our models are universal for realism, and for cartoons, and for anime, and for caricatures.
💖 The project really needs your support and your recommendations and reviews, please do not hesitate to write comments under this post, thank you!
🖼️ Below are demo images made with the pre-release version of Supple Diffusion Small.
🥏 We are ready to announce a new series of Supple Diffusion models, these are new generation diffusion models (about 1-2 weeks left before release).
🦾 The new series aims to take diffusion models to the next level, with performance and versatility as the main goal.
🧐 How will our models be better than others? Firstly, we worked on the CLIP models, now they understand your requests better, it will become easier to process. Secondly, we trained the models with high quality, even better than all our previous ones. Thirdly, you won’t have to keep 20 models on your disk; only 4-6 will be enough.
🗺️ Roadmap:
1. Create Supple Diffusion Small
2. Creating Supple Diffusion Medium
3. Create Supple Diffusion Large
🎆 Our models are universal for realism, and for cartoons, and for anime, and for caricatures.
💖 The project really needs your support and your recommendations and reviews, please do not hesitate to write comments under this post, thank you!
🖼️ Below are demo images made with the pre-release version of Supple Diffusion Small.
-
- 4 replies