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dylanebert 
posted an update 1 day ago
dylanebert 
posted an update 6 days ago
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521
⚙️ Convert .ply to .splat

i've created a simple space to convert .ply gaussian splat files to .splat format

dylanebert/ply-to-splat
dylanebert 
posted an update 20 days ago
akhaliq 
posted an update about 1 month ago
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7347
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
  • 2 replies
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regisss 
posted an update about 1 month ago
dylanebert 
posted an update about 2 months ago
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2284
TRELLIS is now the highest ranked open-source model in the 3D Arena Leaderboard, surpassing InstantMesh

dylanebert/3d-arena
  • 1 reply
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dylanebert 
posted an update about 2 months ago
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2906
blender has AI now
dylanebert 
posted an update about 2 months ago
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3933
🟦 New open-source Image-to-3D model from Microsoft

TRELLIS: Structured 3D Latents for Scalable and Versatile 3D Generation

it's really good! the topology isn't clean, but it's a very very good 3D reference

JeffreyXiang/TRELLIS-image-large
  • 1 reply
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akhaliq 
posted an update 2 months ago
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8360
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
  • 1 reply
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akhaliq 
posted an update 2 months ago
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3900
New model drop in anychat

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

try it here: akhaliq/anychat
dylanebert 
posted an update 2 months ago
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1642
Generate meshes with AI locally in Blender

📢 New open-source release

meshgen, a local blender integration of LLaMa-Mesh, is open source and available now 🤗

get started here: https://github.com/huggingface/meshgen
akhaliq 
posted an update 2 months ago
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2865
anychat

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

try it out there: akhaliq/anychat
regisss 
posted an update 3 months ago
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1409
Interested in performing inference with an ONNX model?⚡️

The Optimum docs about model inference with ONNX Runtime is now much clearer and simpler!

You want to deploy your favorite model on the hub but you don't know how to export it to the ONNX format? You can do it in one line of code as follows:
from optimum.onnxruntime import ORTModelForSequenceClassification

# Load the model from the hub and export it to the ONNX format
model_id = "distilbert-base-uncased-finetuned-sst-2-english"
model = ORTModelForSequenceClassification.from_pretrained(model_id, export=True)

Check out the whole guide 👉 https://huggingface.co/docs/optimum/onnxruntime/usage_guides/models
dylanebert 
posted an update 4 months ago
dylanebert 
posted an update 4 months ago
dylanebert 
posted an update 4 months ago
dylanebert 
posted an update 5 months ago
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2553
Here's a 1-minute video tutorial on how to fine-tune unsloth/llama-3-8b-bnb-4bit with unsloth

Using Roller Coaster Tycoon peep thoughts as an example
akhaliq 
posted an update 8 months ago
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20676
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.
akhaliq 
posted an update 9 months ago
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20964
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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6325
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.