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HICHAM ABADOUR

ApyHTML19

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AI & ML interests

Currently, I'm a Software Engineer Student at ENSAO passionate about LLM / Reasearch Articles /System design /AI Infrastructure / Computer vision .

Recent Activity

reacted to savoji's post with 🔥 4 days ago

A 284B-parameter DeepSeek-V4-Flash now runs on a 64 GB desktop with 0 GPUs: llama-modchip shrinks the checkpoint to 56.56 GiB (1.71 bpw) and decodes at 9.1 to 14.7 tok/s. The standard UD-IQ1_S build is 76.87 GiB, above the ~62.5 GiB usable on a 64 GB Linux box. The repo mixes precisions (routed experts mostly IQ1_S, MLA projections in a custom Fractal-64 type) and patches the CPU kernels. The model plus a 256K KV cache then sits at 57.61 to 58.9 GiB RSS with zero swap. The repo was created on 2026-10-29 and already has 744 stars and 470 points on Hacker News. 📊 The numbers • Size: 56.56 GiB, 20.31 GiB smaller than UD-IQ1_S • Prefill: 25.0 to 44.36 tok/s (+77%) on a 16-core Ryzen 9 9955HX • Decode: 5.91 tok/s unpatched, 9.06 to 9.95 tok/s at Top-6, 11.5 to 14.7 tok/s with the n-gram drafter ⚠️ Caveats • "Lossless" means bit-exact against the same re-quantized checkpoint. Versus UD-IQ1_S the author reports 0.9964 top-logit cosine similarity • The 98.5/100 quality score comes from the author's own gate, and the headline speeds come from one machine • The custom tensor type 40 is rejected by stock llama.cpp, Ollama and LM Studio 🛠️ Try it • Run scripts/check_cpu_readiness.py from the repo: it needs no model download • Run ./modchip bench for speed, then ./modchip eval to check the quality claims yourself Repo: https://github.com/synchopate/llama-modchip HN thread: https://news.ycombinator.com/item?id=50411961 Would you accept a custom runtime for 9 to 15 tok/s from a 284B model on a 64 GB box? 📚 Read it elsewhere • The deep dive on Medium: https://medium.com/p/e47ab3581f58 • The long read on Substack: https://sajjadpakdamansavoji.substack.com/p/llama-modchip-runs-a-284b-deepseek • The thread on X: https://x.com/saavoji/status/2119858773280141526 • Every version on sajjadpsavoji.github.io: https://sajjadpsavoji.github.io/blog#llama-modchip

liked a dataset 5 days ago

mlfoundations/dclm-baseline-1.0

reacted to uuhugg's post with 🔥 26 days ago

A retry can succeed and still be unjustified. RetryBench separates whether an agent’s retry is supported by the visible contract from what happens afterward. After a timeout, success alone does not tell us whether retrying was justified. Try the interactive demo (English / 中文): https://huggingface.co/spaces/uuhugg/retrybench-demo Read the findings and reproduction guide: https://huggingface.co/blog/uuhugg/retrybench-retry-permission How do you evaluate retries in your agents?

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Organizations

ApyHTML19 's models 10

ApyHTML19/Faster-RCNN-Vision-ANN18

Object Detection • Updated Sep 27 • 1

ApyHTML19/MediSeg-Mask-RCNN

Image Segmentation • Updated Sep 25 • 1

ApyHTML19/Mistral-7B-Instruct-v0.3-LoRA-CGI-Marocain

Text Generation • Updated Sep 12

ApyHTML19/Cgi_Model_9M

8.96M • Updated Sep 10 • 6

ApyHTML19/Gemma-2b-It_EU-AI-Reglementation

3B • Updated Aug 29 • 5 • 1

ApyHTML19/NuTag

0.1B • Updated Jul 28 • 22 • 1

ApyHTML19/Granite-Vision-3.3-2B-Q_M_GGUF-F16

Image-Text-to-Text • 3B • Updated Jul 6 • 27

ApyHTML19/qwen-2.5-1.5b-and-llama-3.2-1b

1B • Updated Jun 27 • 140 • 1

ApyHTML19/Qwen2-3B-LoRA-JurisRoute

Updated Jun 26 • 1

ApyHTML19/qwen-onnx-int8

Updated Jun 26 • 5