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kgrabko  new activity 6 days ago
blog-explorers/README:[Support] Community Articles
saisatwik1234567  new activity 7 days ago
blog-explorers/README:Join request pending for a week, anything else needed?
saisatwik1234567  new activity 7 days ago
blog-explorers/README:[Support] Community Articles
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juiceb0xc0de 
posted an update 5 days ago
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3401
Hey I've updated my Hugging Face text generation model search space B-Sides. I always wanted more from HF's model search, so I built one.

I went deeper than the model card, embedding the relevant .json and .py files so you can search for models with custom kernels or exotic imports and specific architecture shapes. You can narrow it down to quantization types and training stacks. If you want to search it and it's not available just make a community post and I would gladly make each query more detailed with an update.

So far over 428,440 text generation models are catalogued with more coming weekly.

On deck: Docker images

https://huggingface.co/spaces/juiceb0xc0de/b-sides
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AtAndDev 
posted an update 5 days ago
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SPECK 2 IS ALREADY OUT: specklabs/Speck2-140M

Pretrained on 4x more tokens than the previous releases (20b vs 5b).
Instruct tuned versions are coming soon.
Very interesting models are coming soon too (hint: super long context).

Thanks for everyone supporting!
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kgrabko 
in blog-explorers/README 6 days ago

[Support] Community Articles

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#5 opened over 2 years ago by
victor
saisatwik1234567 
in blog-explorers/README 7 days ago

Join request pending for a week, anything else needed?

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#28 opened about 1 month ago by
yuncheol-ncdtech

[Support] Community Articles

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#5 opened over 2 years ago by
victor
AtAndDev 
posted an update 8 days ago
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SPECK1.5 IS COMING SOON!
Same 5B token budget but much better corpus quality.

Also getting a ton of downloads, thanks for everyone downloading and liking <3

specklabs
AtAndDev 
posted an update 11 days ago
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NEW SPECK UPDATES:

Just hit #14 and #15 with out FIRST models on Open SLM Leaderboard. The models were trained on 5B tokens, while competing with similarly sized models trained on more than 6-20x the data.

A new base model Speck1.5-140M being trained right now on a higher quality corpus and will be released soon.
SpeckChat3 is coming very soon with 1 million samples, specifically designed to post train small base models.

Also, just to clarify stuff, we will NOT release anything that is NOT MIT licensed EVER. Openness is needed in small language research.

Thanks to everyone supporting the project, and stay tuned for new releases!
juiceb0xc0de 
posted an update 11 days ago
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118
Just dropped a new SAE juiceb0xc0de/gemma-4-e2b-it-SAE

I built this using the Event-Aware SAE Trainer I have been developing. It trained all 35 layers in 1 run and handled the transition from KV owning layers to the KV cache at layers 15 and 16 where MLP width doubles.

I developed the trainer with two goals in mind, keep it accessible and affordable. I wanted to make SAE training simple enough that you don't require a team of frontier developers or a corporate budget. Anyone can choose a model -> hit play -> train a set of SAE's. Zero hyperparameter tweaking. Train every layer.

Optimized features:

- Single block rolling capture approx. 2.9x faster vs full forwards capture zero measured relative error
- Hoist system the model lives in CPU the layers required for SAE training stay in VRAM
- Augmented-Lagrangian λ controller with two sided nudge controller for faster, bidirectional L=0 control
- Dead feature revival preventing dead pileup saving layers from requiring another pass
- Layer-aware early stop triggers prevent training from executing maximum training steps dropping training as low as recorded 1000 steps

juiceb0xc0de/gemma-4-e2b-it-SAE
https://github.com/JuiceB0xC0de/event-aware-SAE-trainer

Drop a new discussion in the community and let me know what you think!
AtAndDev 
posted an update 12 days ago
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1876
SPECK UPDATES:
1 New instruct model tuned on top of Speck1-140M: specklabs/Speck1-140M-Instruct
2 Instruction tuning datasets
2 GGUFs

Much more coming soon:
Speck1.1-140M-Instruct that is post trained on SpeckChat2 will be coming very soon
New base model Speck1.5-140M is coming with a much higher quality corpus

Thanks to everyone who is already supporting the project, and stay tuned for new releases!
  • 3 replies
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AtAndDev 
posted an update 13 days ago
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FIRST SPECK MODEL RELEASED:
specklabs/Speck1-140M

new models coming very soon (both instruct and much better models), with much much higher training scale as i am getting marenostrum5 access soon!
we will be looking at 100b-2t token budgets :)
  • 4 replies
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ayoubkirouane 
submitted a paper to Daily Papers 16 days ago
sdiazlor 
posted an update 17 days ago
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125
P-Bench: new public benchmark for T2I models

- Compare optimized or accelerated endpoints on quality, speed, and price.
- Analyze the paretos
- Explore the samples

Check it here 👉 PrunaAI/P-Bench
ayoubkirouane 
authored a paper 18 days ago
tomaarsen 
posted an update 19 days ago
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🚨 I've just published Sentence Transformers v6.0, introducing MultiVectorEncoder: ColBERT-style late interaction models are now a fourth model type, for training, inference, and interpretation, alongside the dense, sparse, and reranker models! Details:

Where a regular embedding model compresses a whole text into one vector, a multi-vector model keeps one vector per token and scores query against document with the MaxSim operator. That preserves token-level matching information that a single vector has to average away. It is also the state of the art for visual document retrieval, where a text query is matched against page images directly, charts and tables included, with no OCR step in between.

Any PyLate, Stanford ColBERT, or ColPali checkpoint loads straight into the same familiar API: model.encode_query(), model.encode_document(), and model.similarity() just work, whether the documents are texts or page images.

Does it help? LightOn trained LateOn (multi-vector) and DenseOn (dense) on the same data with the same 149M ModernBERT backbone, and the multi-vector model wins on 9 of the 13 NanoBEIR datasets: 0.6868 vs 0.6764 mean NDCG@10. The price is a bigger index, and the new HierarchicalTokenPooling module halves it at roughly no retrieval cost.

Antoine Chaffin, Raphaël Sourty, and I wrote a blog post walking through multi-vector models in practice: loading the various checkpoint formats, encoding and scoring, plugging them into a search stack, running them on page images, and keeping the index affordable. Check it out if you want to get started, or just point your Agent to the URL: https://huggingface.co/blog/multi-vector-encoder

pip install sentence-transformers==6.0.0

Release notes: https://github.com/huggingface/sentence-transformers/releases/tag/v6.0.0
Iker 
submitted a paper to Daily Papers 27 days ago
ayoubkirouane 
submitted a paper to Daily Papers about 1 month ago
ayoubkirouane 
authored a paper about 1 month ago
Iker 
authored 3 papers about 1 month ago