StarCoder 2 and The Stack v2: The Next Generation
Paper • 2402.19173 • Published • 160
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lckr
authored a
paper 4 months ago
Adopt OpenMDW-1.1 license
1
#1 opened 4 months ago
by 
annekethvij
Adopt OpenMDW-1.1 license
1
#1 opened 4 months ago
by annekethvij
Adopt OpenMDW-1.1 license
1
#1 opened 4 months ago
by annekethvij
Adopt OpenMDW-1.1 license
1
#3 opened 4 months ago
by annekethvij
Adopt OpenMDW-1.1 license
1
#1 opened 4 months ago
by annekethvij
Adopt OpenMDW-1.1 license
1
#1 opened 4 months ago
by annekethvij
Adopt OpenMDW-1.1 license
1
#1 opened 4 months ago
by annekethvij
Adopt OpenMDW-1.1 license
1
#1 opened 4 months ago
by annekethvij
Adopt OpenMDW-1.1 license
1
#1 opened 4 months ago
by annekethvij
Adopt OpenMDW-1.1 license
1
#1 opened 4 months ago
by annekethvij
Adopt OpenMDW-1.1 license
1
#1 opened 4 months ago
by annekethvij
Adopt OpenMDW-1.1 license
1
#1 opened 4 months ago
by annekethvij
Post
70671
You may have noticed that my upload of MiMo-V2.5 upload didn't have the author in the model name:
bartowski/MiMo-V2.5-GGUF
Going forward, I plan to upload models from major 1st party developers without the author name attached for cleanliness, I feel it results in a nicer and more expected user experience
I will continue to uploaded fine tunes with that author + "_" appended for clarity, I personally feel it's nice to know at a glance who's tune it is, but it's also for the reason I first started doing it, to avoid it being confused for a new version of the official release
I hope this change makes sense, it seemed most reasonable to me and a poll I did (forever ago, I move slow sometimes) made it seem likely others would find it reasonable as well (feel free to let me know if you disagree, may not change my mind but I do value knowing what others think)
Thanks for downloading :)
bartowski/MiMo-V2.5-GGUF
Going forward, I plan to upload models from major 1st party developers without the author name attached for cleanliness, I feel it results in a nicer and more expected user experience
I will continue to uploaded fine tunes with that author + "_" appended for clarity, I personally feel it's nice to know at a glance who's tune it is, but it's also for the reason I first started doing it, to avoid it being confused for a new version of the official release
I hope this change makes sense, it seemed most reasonable to me and a poll I did (forever ago, I move slow sometimes) made it seem likely others would find it reasonable as well (feel free to let me know if you disagree, may not change my mind but I do value knowing what others think)
Thanks for downloading :)
- 5 replies
MaziyarPanahi
posted an update 6 months ago
Post
5765
Training mRNA Language Models Across 25 Species for $165
We built an end-to-end protein AI pipeline covering structure prediction, sequence design, and codon optimization. After comparing multiple transformer architectures for codon-level language modeling, CodonRoBERTa-large-v2 emerged as the clear winner with a perplexity of 4.10 and a Spearman CAI correlation of 0.40, significantly outperforming ModernBERT. We then scaled to 25 species, trained 4 production models in 55 GPU-hours, and built a species-conditioned system that no other open-source project offers. Complete results, architectural decisions, and runnable code below.
https://huggingface.co/blog/OpenMed/training-mrna-models-25-species
We built an end-to-end protein AI pipeline covering structure prediction, sequence design, and codon optimization. After comparing multiple transformer architectures for codon-level language modeling, CodonRoBERTa-large-v2 emerged as the clear winner with a perplexity of 4.10 and a Spearman CAI correlation of 0.40, significantly outperforming ModernBERT. We then scaled to 25 species, trained 4 production models in 55 GPU-hours, and built a species-conditioned system that no other open-source project offers. Complete results, architectural decisions, and runnable code below.
https://huggingface.co/blog/OpenMed/training-mrna-models-25-species
- 1 reply
MaziyarPanahi
posted an update 6 months ago
Post
2457
We annotated 119K medical images with two frontier VLMs (Qwen 3.5, Kimi K2.5), cross-validated at 93% agreement, and produced 110K training records, all for under $500. Fine-tuning 3 small models (2-3B params) improved all benchmarks: best model reaches +15.0% average exact match.
Everything is open-sourced: datasets, adapters, and code.
https://huggingface.co/blog/OpenMed/synthvision
Everything is open-sourced: datasets, adapters, and code.
https://huggingface.co/blog/OpenMed/synthvision
- 2 replies
MaziyarPanahi
posted an update 7 months ago
Post
5017
DNA, mRNA, proteins, AI. I spent the last year going deep into computational biology as an ML engineer. This is Part I of what I found. 🧬
In 2024, AlphaFold won the Nobel Prize in Chemistry.
By 2026, the open-source community had built alternatives that outperform it.
That's the story I find most interesting about protein AI right now. Not just the science (which is incredible), but the speed at which open-source caught up. Multiple teams, independently, reproduced and then exceeded AlphaFold 3's accuracy with permissive licenses. The field went from prediction to generation: we're not just modeling known proteins anymore, we're designing new ones.
I spent months mapping this landscape for ML engineers. What the architectures actually are (spoiler: transformers and diffusion models), which tools to use for what, and which ones you can actually ship commercially.
New post on the Hugging Face blog: https://huggingface.co/blog/MaziyarPanahi/protein-ai-landscape
Hope you all enjoy! 🤗
In 2024, AlphaFold won the Nobel Prize in Chemistry.
By 2026, the open-source community had built alternatives that outperform it.
That's the story I find most interesting about protein AI right now. Not just the science (which is incredible), but the speed at which open-source caught up. Multiple teams, independently, reproduced and then exceeded AlphaFold 3's accuracy with permissive licenses. The field went from prediction to generation: we're not just modeling known proteins anymore, we're designing new ones.
I spent months mapping this landscape for ML engineers. What the architectures actually are (spoiler: transformers and diffusion models), which tools to use for what, and which ones you can actually ship commercially.
New post on the Hugging Face blog: https://huggingface.co/blog/MaziyarPanahi/protein-ai-landscape
Hope you all enjoy! 🤗
- 2 replies
annekethvij
authored 2
papers 7 months ago
pranavkr
authored a
paper 7 months ago