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Ory Smith
oraculus541
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mradermacher/model_requests:https://huggingface.co/vanta-research/wraith-coder-7b
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#1543 opened about 10 hours ago
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oraculus541
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unmodeled-tyler's
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NEW Research Preview Models ⚛️ :
vanta-research/atom-v1-preview-12b
vanta-research/atom-v1-preview-8b
Last week we released Atom V1 4B and Atom V1 8B, our first two preview releases in Project Atom from VANTA Research - a larger effort to refine and scale the Atom persona from 4B to 400B+.
Today we are excited to share Atom V1 12B! This preview release is built on Google's Gemma3 12B architecture, and fine tuned for exploratory and collaborative interaction.
Atom is intentionally trained to not simply be an informational source, but a partner in thought. As such, the model regularly and consistently returns thoughtful questions at the end of it's responses. This is designed not only to keep the interaction engaging, but to encourage deeper thought/exploration between the user and model.
As always, feedback is welcome as we continue to refine our approach to Project Atom, and human-AI collaboration in general.
We hope you enjoy!
vanta-research/atom-v1-preview-12b
vanta-research/atom-v1-preview-8b
Last week we released Atom V1 4B and Atom V1 8B, our first two preview releases in Project Atom from VANTA Research - a larger effort to refine and scale the Atom persona from 4B to 400B+.
Today we are excited to share Atom V1 12B! This preview release is built on Google's Gemma3 12B architecture, and fine tuned for exploratory and collaborative interaction.
Atom is intentionally trained to not simply be an informational source, but a partner in thought. As such, the model regularly and consistently returns thoughtful questions at the end of it's responses. This is designed not only to keep the interaction engaging, but to encourage deeper thought/exploration between the user and model.
As always, feedback is welcome as we continue to refine our approach to Project Atom, and human-AI collaboration in general.
We hope you enjoy!
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Kseniase's
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12 Types of JEPA
Since Yann LeCun together with Randall Balestriero released a new paper on JEPA (Joint-Embedding Predictive Architecture), laying out its theory and introducing an efficient practical version called LeJEPA, we figured you might need even more JEPA. Here are 7 recent JEPA variants plus 5 iconic ones:
1. LeJEPA → LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics (2511.08544)
Explains a full theory for JEPAs, defining the “ideal” JEPA embedding as an isotropic Gaussian, and proposes the SIGReg objective to push JEPA toward this ideal, resulting in practical LeJEPA
2. JEPA-T → JEPA-T: Joint-Embedding Predictive Architecture with Text Fusion for Image Generation (2510.00974)
A text-to-image model that tokenizes images and captions with a joint predictive Transformer, enhances fusion with cross-attention and text embeddings before training loss, and generates images by iteratively denoising visual tokens conditioned on text
3. Text-JEPA → Speaking in Words, Thinking in Logic: A Dual-Process Framework in QA Systems (2507.20491)
Converts natural language into first-order logic, with a Z3 solver handling reasoning, enabling efficient, explainable QA with far lower compute than large LLMs
4. N-JEPA (Noise-based JEPA) → Improving Joint Embedding Predictive Architecture with Diffusion Noise (2507.15216)
Connects self-supervised learning with diffusion-style noise by using noise-based masking and multi-level schedules, especially improving visual classification
5. SparseJEPA → SparseJEPA: Sparse Representation Learning of Joint Embedding Predictive Architectures (2504.16140)
Adds sparse representation learning to make embeddings more interpretable and efficient. It groups latent variables by shared semantic structure using a sparsity penalty while preserving accuracy
6. TS-JEPA (Time Series JEPA) → Joint Embeddings Go Temporal (2509.25449)
Adapts JEPA to time-series by learning latent self-supervised representations and predicting future latents for robustness to noise and confounders
Read further below ↓
It you like it, also subscribe to the Turing Post: https://www.turingpost.com/subscribe
Since Yann LeCun together with Randall Balestriero released a new paper on JEPA (Joint-Embedding Predictive Architecture), laying out its theory and introducing an efficient practical version called LeJEPA, we figured you might need even more JEPA. Here are 7 recent JEPA variants plus 5 iconic ones:
1. LeJEPA → LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics (2511.08544)
Explains a full theory for JEPAs, defining the “ideal” JEPA embedding as an isotropic Gaussian, and proposes the SIGReg objective to push JEPA toward this ideal, resulting in practical LeJEPA
2. JEPA-T → JEPA-T: Joint-Embedding Predictive Architecture with Text Fusion for Image Generation (2510.00974)
A text-to-image model that tokenizes images and captions with a joint predictive Transformer, enhances fusion with cross-attention and text embeddings before training loss, and generates images by iteratively denoising visual tokens conditioned on text
3. Text-JEPA → Speaking in Words, Thinking in Logic: A Dual-Process Framework in QA Systems (2507.20491)
Converts natural language into first-order logic, with a Z3 solver handling reasoning, enabling efficient, explainable QA with far lower compute than large LLMs
4. N-JEPA (Noise-based JEPA) → Improving Joint Embedding Predictive Architecture with Diffusion Noise (2507.15216)
Connects self-supervised learning with diffusion-style noise by using noise-based masking and multi-level schedules, especially improving visual classification
5. SparseJEPA → SparseJEPA: Sparse Representation Learning of Joint Embedding Predictive Architectures (2504.16140)
Adds sparse representation learning to make embeddings more interpretable and efficient. It groups latent variables by shared semantic structure using a sparsity penalty while preserving accuracy
6. TS-JEPA (Time Series JEPA) → Joint Embeddings Go Temporal (2509.25449)
Adapts JEPA to time-series by learning latent self-supervised representations and predicting future latents for robustness to noise and confounders
Read further below ↓
It you like it, also subscribe to the Turing Post: https://www.turingpost.com/subscribe
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3 days ago
https://huggingface.co/vanta-research/atom-v1-preview-12b
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#1532 opened 4 days ago
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oraculus541
https://huggingface.co/vanta-research/atom-v1-preview-8b
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#1520 opened 8 days ago
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oraculus541
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onekq's
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10 days ago
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Instead of architectural upgade, each major model drop nowadays perfects a regional innovation. What Kimi brought to spot light this time is quantization aware training (QAT). I wrote an article to explain it and why it matters to reasoning models.
https://huggingface.co/blog/onekq/qat-bonsai
If you are interested in this kind of posts, I will introduce the Muon optimizers, another technology behind Kimi success.
https://huggingface.co/blog/onekq/qat-bonsai
If you are interested in this kind of posts, I will introduce the Muon optimizers, another technology behind Kimi success.
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