ThreadWeaver: Adaptive Threading for Efficient Parallel Reasoning in Language Models
Paper
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2512.07843
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Published
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18
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akhaliq
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a
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1 day ago
akhaliq
authored
a
paper
about 2 months ago
mortimerp9
authored
a
paper
3 months ago
Post
46442
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: https://huggingface.co/spaces/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: https://huggingface.co/spaces/akhaliq/anychat
- 5 replies
elbayadm
authored
4
papers
12 months ago
Token-level and sequence-level loss smoothing for RNN language models
Paper
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1805.05062
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Published
Efficient Wait-k Models for Simultaneous Machine Translation
Paper
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2005.08595
•
Published
Added Toxicity Mitigation at Inference Time for Multimodal and Massively Multilingual Translation
Paper
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2311.06532
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Published
Large Concept Models: Language Modeling in a Sentence Representation Space
Paper
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2412.08821
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Published
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17
Post
45599
QwQ-32B-Preview is now available in anychat
A reasoning model that is competitive with OpenAI o1-mini and o1-preview
try it out: https://huggingface.co/spaces/akhaliq/anychat
A reasoning model that is competitive with OpenAI o1-mini and o1-preview
try it out: https://huggingface.co/spaces/akhaliq/anychat
- 1 reply
Post
5024
New model drop in anychat
allenai/Llama-3.1-Tulu-3-8B is now available
try it here: https://huggingface.co/spaces/akhaliq/anychat
allenai/Llama-3.1-Tulu-3-8B is now available
try it here: https://huggingface.co/spaces/akhaliq/anychat
Post
3791
anychat
supports chatgpt, gemini, perplexity, claude, meta llama, grok all in one app
try it out there: https://huggingface.co/spaces/akhaliq/anychat
supports chatgpt, gemini, perplexity, claude, meta llama, grok all in one app
try it out there: https://huggingface.co/spaces/akhaliq/anychat
sravyapopuri388
authored
a
paper
about 1 year ago
sravyapopuri388
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over 1 year ago
mavlyutovrus
authored
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over 1 year ago
hygong
authored
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over 1 year ago
Post
21188
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.
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.
Post
7864
Thanks to
@OzzyGT
for pushing the new Anyline preprocessor to https://github.com/huggingface/controlnet_aux. Now you can use the
TheMistoAI/MistoLine ControlNet with Diffusers completely.
Here's a demo for you: radames/MistoLine-ControlNet-demo
Super resolution version: radames/Enhance-This-HiDiffusion-SDXL
Here's a demo for you: radames/MistoLine-ControlNet-demo
Super resolution version: radames/Enhance-This-HiDiffusion-SDXL
from controlnet_aux import AnylineDetector
anyline = AnylineDetector.from_pretrained(
"TheMistoAI/MistoLine", filename="MTEED.pth", subfolder="Anyline"
).to("cuda")
source = Image.open("source.png")
result = anyline(source, detect_resolution=1280)Post
21381
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.
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.
Post
7052
At Google I/O 2024, we're collaborating with the Google Visual Blocks team (https://visualblocks.withgoogle.com) to release custom Hugging Face nodes. Visual Blocks for ML is a browser-based tool that allows users to create machine learning pipelines using a visual interface. We're launching nodes with Transformers.js, running models on the browser, as well as server-side nodes running Transformers pipeline tasks and LLMs using our hosted inference. With
@Xenova
@JasonMayes
You can learn more about it here https://huggingface.co/blog/radames/hugging-face-google-visual-blocks
Source-code for the custom nodes:
https://github.com/huggingface/visual-blocks-custom-components
You can learn more about it here https://huggingface.co/blog/radames/hugging-face-google-visual-blocks
Source-code for the custom nodes:
https://github.com/huggingface/visual-blocks-custom-components
Post
2165
AI-town now runs on Hugging Face Spaces with our API for LLMs and embeddings, including the open-source Convex backend, all in one container. Easy to duplicate and config on your own
Demo: radames/ai-town
Instructions: https://github.com/radames/ai-town-huggingface
Demo: radames/ai-town
Instructions: https://github.com/radames/ai-town-huggingface
- 9 replies