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ehristoforu 
posted an update 27 days ago
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3035
✒️ 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.
akhaliq 
posted an update 30 days ago
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5422
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
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akhaliq 
posted an update about 2 months ago
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6533
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
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akhaliq 
posted an update about 2 months ago
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3824
New model drop in anychat

allenai/Llama-3.1-Tulu-3-8B is now available

try it here: akhaliq/anychat
akhaliq 
posted an update about 2 months ago
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2793
anychat

supports chatgpt, gemini, perplexity, claude, meta llama, grok all in one app

try it out there: akhaliq/anychat
muneebable 
updated a model 3 months ago
ehristoforu 
posted an update 6 months ago
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4187
😏 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!
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ehristoforu 
posted an update 7 months ago
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6366
🤗 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.
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ehristoforu 
posted an update 7 months ago
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3863
🦾 Hello, I present Visionix Alpha - a new hyper-realistic model based on SDXL. The main difference from all existing realism models is the attention to detail, that is, I improved not only hyperrealism, but also the overall aesthetics, anatomy, the beauty of nature, and more, and the model also has the most different faces. This model is suitable not only for realistic photos, but also for generating 2.5d anime, realistic cartoons and more.

🤗 Model on HF: ehristoforu/Visionix-alpha
🥏 Model on CivitAI: https://civitai.com/models/505719
🪄 Playground (with base and inpaint model): ehristoforu/Visionix-Playground

✏️ Inpaint version on HF: ehristoforu/Visionix-alpha-inpainting
🖋️ Inpaint version on CivitAI: https://civitai.com/models/505719?modelVersionId=563519
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ehristoforu 
posted an update 8 months ago
ehristoforu 
posted an update 8 months ago
ehristoforu 
posted an update 8 months ago
ehristoforu 
posted an update 8 months ago
akhaliq 
posted an update 8 months ago
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20652
Phased Consistency Model

Phased Consistency Model (2405.18407)

The consistency model (CM) has recently made significant progress in accelerating the generation of diffusion models. However, its application to high-resolution, text-conditioned image generation in the latent space (a.k.a., LCM) remains unsatisfactory. In this paper, we identify three key flaws in the current design of LCM. We investigate the reasons behind these limitations and propose the Phased Consistency Model (PCM), which generalizes the design space and addresses all identified limitations. Our evaluations demonstrate that PCM significantly outperforms LCM across 1--16 step generation settings. While PCM is specifically designed for multi-step refinement, it achieves even superior or comparable 1-step generation results to previously state-of-the-art specifically designed 1-step methods. Furthermore, we show that PCM's methodology is versatile and applicable to video generation, enabling us to train the state-of-the-art few-step text-to-video generator.
ehristoforu 
posted an update 8 months ago
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2130
😐 Hello, there are a couple of interesting things. The first is that I will soon release several pretty cool SDXL models, the second is a little sad, I conducted long-term tests of training and merging of XL models and realized that XL will not improve soon, the architecture will not allow us to continue pushing realism and other interesting things into it, the entire community has brought XL closer to the maximum ideal on its architecture.
ehristoforu 
posted an update 8 months ago
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🤗 SDXL Flash

✨️ Introducing the new fast model SDXL Flash (Mini), we learned that all fast XL models work fast, but the quality decreases, and we also made a fast model, but it is not as fast as LCM, Turbo, Lightning and Hyper, but the quality is higher. Below you will see the study with steps and cfg.

🚀 Features of mini model:
It weighs less, consumes less video memory and other resources, and the quality has not dropped much.

👑 Our faster than regular model is better in quality than the coolest modern models such as JuggernautXL X, FluentlyXL v4 and others.

SDXL Flash: sd-community/sdxl-flash
SDXL Flash Mini: sd-community/sdxl-flash-mini
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akhaliq 
posted an update 8 months ago
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20945
Chameleon

Mixed-Modal Early-Fusion Foundation Models

Chameleon: Mixed-Modal Early-Fusion Foundation Models (2405.09818)

We present Chameleon, a family of early-fusion token-based mixed-modal models capable of understanding and generating images and text in any arbitrary sequence. We outline a stable training approach from inception, an alignment recipe, and an architectural parameterization tailored for the early-fusion, token-based, mixed-modal setting. The models are evaluated on a comprehensive range of tasks, including visual question answering, image captioning, text generation, image generation, and long-form mixed modal generation. Chameleon demonstrates broad and general capabilities, including state-of-the-art performance in image captioning tasks, outperforms Llama-2 in text-only tasks while being competitive with models such as Mixtral 8x7B and Gemini-Pro, and performs non-trivial image generation, all in a single model. It also matches or exceeds the performance of much larger models, including Gemini Pro and GPT-4V, according to human judgments on a new long-form mixed-modal generation evaluation, where either the prompt or outputs contain mixed sequences of both images and text. Chameleon marks a significant step forward in a unified modeling of full multimodal documents.
akhaliq 
posted an update 9 months ago
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6312
A Careful Examination of Large Language Model Performance on Grade School Arithmetic

A Careful Examination of Large Language Model Performance on Grade School Arithmetic (2405.00332)

Large language models (LLMs) have achieved impressive success on many benchmarks for mathematical reasoning. However, there is growing concern that some of this performance actually reflects dataset contamination, where data closely resembling benchmark questions leaks into the training data, instead of true reasoning ability. To investigate this claim rigorously, we commission Grade School Math 1000 (GSM1k). GSM1k is designed to mirror the style and complexity of the established GSM8k benchmark, the gold standard for measuring elementary mathematical reasoning. We ensure that the two benchmarks are comparable across important metrics such as human solve rates, number of steps in solution, answer magnitude, and more. When evaluating leading open- and closed-source LLMs on GSM1k, we observe accuracy drops of up to 13%, with several families of models (e.g., Phi and Mistral) showing evidence of systematic overfitting across almost all model sizes. At the same time, many models, especially those on the frontier, (e.g., Gemini/GPT/Claude) show minimal signs of overfitting. Further analysis suggests a positive relationship (Spearman's r^2=0.32) between a model's probability of generating an example from GSM8k and its performance gap between GSM8k and GSM1k, suggesting that many models may have partially memorized GSM8k.
ehristoforu 
posted an update 9 months ago
akhaliq 
posted an update 9 months ago
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4798
Octopus v4

Graph of language models

Octopus v4: Graph of language models (2404.19296)

Language models have been effective in a wide range of applications, yet the most sophisticated models are often proprietary. For example, GPT-4 by OpenAI and various models by Anthropic are expensive and consume substantial energy. In contrast, the open-source community has produced competitive models, like Llama3. Furthermore, niche-specific smaller language models, such as those tailored for legal, medical or financial tasks, have outperformed their proprietary counterparts. This paper introduces a novel approach that employs functional tokens to integrate multiple open-source models, each optimized for particular tasks. Our newly developed Octopus v4 model leverages functional tokens to intelligently direct user queries to the most appropriate vertical model and reformat the query to achieve the best performance. Octopus v4, an evolution of the Octopus v1, v2, and v3 models, excels in selection and parameter understanding and reformatting. Additionally, we explore the use of graph as a versatile data structure that effectively coordinates multiple open-source models by harnessing the capabilities of the Octopus model and functional tokens.