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1024m  authored a paper about 3 hours ago
Improving Multilingual Capabilities with Cultural and Local Knowledge in Large Language Models While Enhancing Native Performance
1024m  authored a paper 5 days ago
Kaleidoscope: In-language Exams for Massively Multilingual Vision Evaluation
bstadt  authored a paper 14 days ago
CoRNStack: High-Quality Contrastive Data for Better Code Ranking
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HF-Party's activity

csabakecskemeti 
posted an update 6 days ago
Xenova 
authored a paper 7 days ago
csabakecskemeti 
posted an update 7 days ago
zamal 
posted an update 12 days ago
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1744
🚀 DeepGit Lite is live! 🔍✨

Hey folks!
Just launched DeepGit Lite — a lighter version of DeepGit with fewer components under the hood.
It won’t perform quite like the full powerhouse, but it’s great for a quick peek and first-hand feel! ⚙️👀

Give it a spin and tell us what you think!
👉 Try it here zamal/DeepGit-lite
#opensource #DeepGit #gradio #githubresearch
  • 1 reply
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bstadt 
authored 2 papers 14 days ago
zamal 
posted an update 15 days ago
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2534
DeepGit: Your GitHub Gold Digger! 💰🚀
Hey Hugging Face gang! Meet DeepGit—my open-source sidekick that rips through GitHub to snag repos that fit you. Done with dead-end searches? Me too. Built it with LangGraph and some dope tricks:
Embeddings grab the good stuff (HF magic, baby!)

Re-ranking nails the best picks

Snoops docs, code, and buzz in one slick flow

Drops a clean list of hidden gems 💎

Unearth that sneaky ML lib or Python gem—run python app.py or langgraph dev and boom! Peek it at https://github.com/zamalali/DeepGit. Fork it, tweak it, love it—Docker’s in, HF vibes are strong. Drop a 🌟 or a crazy idea—I’m pumped to jam with you all! 🪂
Aurelien-Morgan 
posted an update 17 days ago
osanseviero 
authored a paper 18 days ago
csabakecskemeti 
posted an update 22 days ago
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3356
I'm collecting llama-bench results for inference with a llama 3.1 8B q4 and q8 reference models on varoius GPUs. The results are average of 5 executions.
The system varies (different motherboard and CPU ... but that probably that has little effect on the inference performance).

https://devquasar.com/gpu-gguf-inference-comparison/
the exact models user are in the page

I'd welcome results from other GPUs is you have access do anything else you've need in the post. Hopefully this is useful information everyone.
csabakecskemeti 
posted an update 24 days ago
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2381
Managed to get my hands on a 5090FE, it's beefy

| llama 8B Q8_0 | 7.95 GiB | 8.03 B | CUDA | 99 | pp512 | 12207.44 ± 481.67 |
| llama 8B Q8_0 | 7.95 GiB | 8.03 B | CUDA | 99 | tg128 | 143.18 ± 0.18 |

Comparison with others GPUs
http://devquasar.com/gpu-gguf-inference-comparison/
csabakecskemeti 
posted an update 28 days ago
csabakecskemeti 
posted an update about 1 month ago
csabakecskemeti 
posted an update about 1 month ago
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827
Fine tuning on the edge. Pushing the MI100 to it's limits.
QWQ-32B 4bit QLORA fine tuning
VRAM usage 31.498G/31.984G :D

  • 4 replies
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zamal 
posted an update about 1 month ago
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2002
🚀 ftBoost is LIVE – Stop Struggling with Fine-Tuning Data!

Alright folks, if you’re tired of manually crafting fine-tuning datasets, ftBoost is here to do the heavy lifting. One-click, LangChain-Groq-powered data augmentation that scales your training data in OpenAI, Gemini, Mistral, and LLaMA formats—automatically.

🔥 What’s inside?
✅ Smart Augmentations – Paraphrasing, back translation, synonym swapping & synthetic noise.
✅ No more JSONL headaches – Auto-formats everything for OpenAI, Gemini, Mistral & LLaMA.
✅ Custom tuning – Adjust similarity, diversity, and fluency in real-time.
✅ Upload, generate, download – That’s it.

⚡ If you’re fine-tuning LLMs, this will save you hours.

🚀 Try it now: 👉 zamal/Finetune-Boost

🌟 Give us a star on GitHub!

Let me know what you think & how it boosts your workflow! 🔥
csabakecskemeti 
posted an update about 1 month ago
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1966
-UPDATED-
4bit inference is working! The blogpost is updated with code snippet and requirements.txt
https://devquasar.com/uncategorized/all-about-amd-and-rocm/
-UPDATED-
I've played around with an MI100 and ROCm and collected my experience in a blogpost:
https://devquasar.com/uncategorized/all-about-amd-and-rocm/
Unfortunately I've could not make inference or training work with model loaded in 8bit or use BnB, but did everything else and documented my findings.
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csabakecskemeti 
posted an update about 2 months ago
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2789
Testing Training on AMD/ROCm the first time!

I've got my hands on an AMD Instinct MI100. It's about the same price used as a V100 but on paper has more TOPS (V100 14TOPS vs MI100 23TOPS) also the HBM has faster clock so the memory bandwidth is 1.2TB/s.
For quantized inference it's a beast (MI50 was also surprisingly fast)

For LORA training with this quick test I could not make the bnb config works so I'm running the FT on the fill size model.

Will share all the install, setup and setting I've learned in a blog post, together with the cooling shroud 3D design.
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lysandre 
posted an update about 2 months ago
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6270
SmolVLM-2 and SigLIP-2 are now part of transformers in dedicated releases!

They're added on top of the v4.49.0 release, and can be installed from the following tags: v4.49.0-SmolVLM-2 and v4.49.0-SigLIP-2.

This marks a new beginning for the release process of transformers. For the past five years, we've been doing monthly releases featuring many models (v4.49.0, the latest release, features 9 new architectures).

Starting with SmolVLM-2 & SigLIP2, we'll now additionally release tags supporting new models on a stable branch. These models are therefore directly available for use by installing from the tag itself. These tags will continue to be updated with fixes applied to these models.

Going forward, continue expecting software releases following semantic versioning: v4.50.0 will have ~10 new architectures compared to v4.49.0, as well as a myriad of new features, improvements and bug fixes. Accompanying these software releases, we'll release tags offering brand new models as fast as possible, to make them accessible to all immediately.
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csabakecskemeti 
posted an update about 2 months ago
Xenova 
posted an update 2 months ago
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12935
We did it. Kokoro TTS (v1.0) can now run 100% locally in your browser w/ WebGPU acceleration. Real-time text-to-speech without a server. ⚡️

Generate 10 seconds of speech in ~1 second for $0.

What will you build? 🔥
webml-community/kokoro-webgpu

The most difficult part was getting the model running in the first place, but the next steps are simple:
✂️ Implement sentence splitting, allowing for streamed responses
🌍 Multilingual support (only phonemization left)

Who wants to help?
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