HajMADOBANANA
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mahdinozhati
MAHDI1235
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MAHDI1235/hajMADOBANANA published
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MAHDI1235/hajMADOBANANA reacted
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Ujjwal-Tyagi's
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Public reports allege that Anthropic gobbled up trillions of tokens of copyrighted material and public data to build their castle. 🏰📄 Now that they're sitting on top, they're begging for special laws to protect their profits while pulling the ladder up behind them. 🪜🚫
But the hypocrisy meter just broke! 📉 They are accusing Chinese labs like DeepSeek, Minimax, and Kimi of "huge distillation attacks. The Reality is that You can't just loot the entire internet's library, lock the door, and then sue everyone else for reading through the window. Stop trying to gatekeep the tech you didn't own in the first place. Read the complete article on it: https://huggingface.co/blog/Ujjwal-Tyagi/the-dark-underbelly-of-anthropic Organizations
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updated a Space 6 days ago
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reacted to Ujjwal-Tyagi's post with 😎 6 days ago
Post
2783
Public reports allege that Anthropic gobbled up trillions of tokens of copyrighted material and public data to build their castle. 🏰📄 Now that they're sitting on top, they're begging for special laws to protect their profits while pulling the ladder up behind them. 🪜🚫
But the hypocrisy meter just broke! 📉 They are accusing Chinese labs like DeepSeek, Minimax, and Kimi of "huge distillation attacks. The Reality is that You can't just loot the entire internet's library, lock the door, and then sue everyone else for reading through the window. Stop trying to gatekeep the tech you didn't own in the first place. Read the complete article on it: https://huggingface.co/blog/Ujjwal-Tyagi/the-dark-underbelly-of-anthropic
But the hypocrisy meter just broke! 📉 They are accusing Chinese labs like DeepSeek, Minimax, and Kimi of "huge distillation attacks. The Reality is that You can't just loot the entire internet's library, lock the door, and then sue everyone else for reading through the window. Stop trying to gatekeep the tech you didn't own in the first place. Read the complete article on it: https://huggingface.co/blog/Ujjwal-Tyagi/the-dark-underbelly-of-anthropic
- 3 replies
reacted to BibbyResearch's post with 👀 6 days ago
Post
3046
Announcing📢 on HuggingFace 🤗
An autonomous agent for drug discovery research. Like Claude Code, but for biology.
BibbyResearch/Open-CellType-GPT
Quick install
Detects Python 3.10+, installs via
Ask questions in natural language. celltype-cli plans the analysis, selects the right tools, executes them, validates results, and returns data-backed conclusions.
BibbyResearch/Open-CellType-GPT
An autonomous agent for drug discovery research. Like Claude Code, but for biology.
BibbyResearch/Open-CellType-GPT
Quick install
curl -fsSL https://raw.githubusercontent.com/celltype/cli/main/install.sh | bashDetects Python 3.10+, installs via
pipx or pip, and launches an interactive setup wizard.Ask questions in natural language. celltype-cli plans the analysis, selects the right tools, executes them, validates results, and returns data-backed conclusions.
BibbyResearch/Open-CellType-GPT
reacted to aufklarer's post with 🚀 6 days ago
Post
2499
PersonaPlex-7B on Apple Silicon (Swift + MLX Swift)
NVIDIA PersonaPlex is a full-duplex speech-to-speech model — it can listen while it speaks, which enables more natural conversational behaviors like interruptions, overlaps, and quick backchannels.
We put together a native Swift implementation using MLX Swift so it can run locally on Apple Silicon, along with a 4-bit MLX conversion and a small CLI/demo to make it easy to try out.
If you’re interested in on-device voice agents (or just want to see what full-duplex S2S looks like in a real Swift codebase), the details and setup notes are here:
Blog post: https://blog.ivan.digital/nvidia-personaplex-7b-on-apple-silicon-full-duplex-speech-to-speech-in-native-swift-with-mlx-0aa5276f2e23
Repo: https://github.com/ivan-digital/qwen3-asr-swift
NVIDIA PersonaPlex is a full-duplex speech-to-speech model — it can listen while it speaks, which enables more natural conversational behaviors like interruptions, overlaps, and quick backchannels.
We put together a native Swift implementation using MLX Swift so it can run locally on Apple Silicon, along with a 4-bit MLX conversion and a small CLI/demo to make it easy to try out.
If you’re interested in on-device voice agents (or just want to see what full-duplex S2S looks like in a real Swift codebase), the details and setup notes are here:
Blog post: https://blog.ivan.digital/nvidia-personaplex-7b-on-apple-silicon-full-duplex-speech-to-speech-in-native-swift-with-mlx-0aa5276f2e23
Repo: https://github.com/ivan-digital/qwen3-asr-swift
reacted to robtacconelli's post with 🔥 6 days ago
Post
3604
🏆 Nacrith: a 135M model that out-compresses everything on natural language
What if a tiny LM could compress english text better than _every_ compressor out there — classical or neural, small or large?
Nacrith pairs SmolLM2-135M with an ensemble of online predictors and high-precision arithmetic coding.
What's inside
The standard LLM+arithmetic coding approach wastes ~75% of CDF precision on large vocabularies. Our CDF-24 fix alone recovers 0.5 bpb. On top: a token N-gram that skips the GPU on predictable tokens, an adaptive bias head, llama.cpp backend (7× faster than PyTorch), multi-GPU parallel compression, and a binary file format (NC06) — the first LLM-based binary compressor we know of.
Runs on a GTX 1050 Ti. ~500 MB weights, ~1.2 GB VRAM per worker.
💻 Code: https://github.com/robtacconelli/Nacrith-GPU
⭐ Space: robtacconelli/Nacrith-GPU
📄 Paper: Nacrith: Neural Lossless Compression via Ensemble Context Modeling and High-Precision CDF Coding (2602.19626)
Try it, break it, share your results — all feedback welcome. ⭐ on the repo appreciated!
Results across all systems we tested:
- alice29.txt → 0.918 bpb (−44% vs CMIX, −20% vs ts_zip) — below the 2nd-order Shannon entropy bound
- enwik8 (100 MB) → 0.9389 bpb (−8% vs FineZip/LLMZip's 8B model, −15% vs ts_zip)
- Unseen text → 0.723 bpb on a doc published after training cutoff — no memorization, 26% better than FineZip/LLMZip on the same model
SmolLM2-135M by
HuggingFaceTB
What if a tiny LM could compress english text better than _every_ compressor out there — classical or neural, small or large?
Nacrith pairs SmolLM2-135M with an ensemble of online predictors and high-precision arithmetic coding.
What's inside
The standard LLM+arithmetic coding approach wastes ~75% of CDF precision on large vocabularies. Our CDF-24 fix alone recovers 0.5 bpb. On top: a token N-gram that skips the GPU on predictable tokens, an adaptive bias head, llama.cpp backend (7× faster than PyTorch), multi-GPU parallel compression, and a binary file format (NC06) — the first LLM-based binary compressor we know of.
Runs on a GTX 1050 Ti. ~500 MB weights, ~1.2 GB VRAM per worker.
💻 Code: https://github.com/robtacconelli/Nacrith-GPU
⭐ Space: robtacconelli/Nacrith-GPU
📄 Paper: Nacrith: Neural Lossless Compression via Ensemble Context Modeling and High-Precision CDF Coding (2602.19626)
Try it, break it, share your results — all feedback welcome. ⭐ on the repo appreciated!
Results across all systems we tested:
- alice29.txt → 0.918 bpb (−44% vs CMIX, −20% vs ts_zip) — below the 2nd-order Shannon entropy bound
- enwik8 (100 MB) → 0.9389 bpb (−8% vs FineZip/LLMZip's 8B model, −15% vs ts_zip)
- Unseen text → 0.723 bpb on a doc published after training cutoff — no memorization, 26% better than FineZip/LLMZip on the same model
SmolLM2-135M by
- 1 reply
reacted to danielhanchen's post with 🤗 6 days ago
Post
3345
100,000+ models trained with Unsloth have now been open-sourced on 🤗Hugging Face! 🦥
Here are the most popular ones you can run local:
1. TeichAI - GLM-4.7-Flash distilled from Claude 4.5 Opus (high)
2. Zed - Qwen Coder 7B fine-tuned for stronger coding
3. DavidAU - Llama-3.3-8B distilled from Claude 4.5 Opus (high)
4. huihui - gpt-oss made “abliberated”
Links to models:
1. TeichAI: TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF
2. Zed: zed-industries/zeta
3. DavidAU: DavidAU/Llama3.3-8B-Instruct-Thinking-Claude-4.5-Opus-High-Reasoning
4. huihui: huihui-ai/Huihui-gpt-oss-20b-BF16-abliterated
See all the 100K latest models fine-tuned with Unsloth here: https://huggingface.co/models?other=u
Here are the most popular ones you can run local:
1. TeichAI - GLM-4.7-Flash distilled from Claude 4.5 Opus (high)
2. Zed - Qwen Coder 7B fine-tuned for stronger coding
3. DavidAU - Llama-3.3-8B distilled from Claude 4.5 Opus (high)
4. huihui - gpt-oss made “abliberated”
Links to models:
1. TeichAI: TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF
2. Zed: zed-industries/zeta
3. DavidAU: DavidAU/Llama3.3-8B-Instruct-Thinking-Claude-4.5-Opus-High-Reasoning
4. huihui: huihui-ai/Huihui-gpt-oss-20b-BF16-abliterated
See all the 100K latest models fine-tuned with Unsloth here: https://huggingface.co/models?other=u
- 2 replies