ModelCloud @ModelCloudAi
Github: https://t.co/QfUNDk4J5Q 🤗 https://t.co/YgYXzLjEmv Our mission is to make ai accessible to any human and robot. {In Stealth-Mode} Earth - Epoch 2.0 Joined June 2024-
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🚀 For quant nerds & open-model enjoyers 🧠⚡ GPT-QModel 7.1 main branch just added quantization support for PoolsideAI’s brand-new Laguna models 🌊🏝️ Huge congrats to the Poolside AI team for these open agent-ready models 🤖🔥 @poolsideai @eisokant @jasoncwarner
👏GPT-QModel v7.0 major release now on pypi. Now 150x smaller footprint due to just-in-time kernel compilation. 👀
🚀 GPT-QModel v7.0.0 is here! 🔥 Huawei Ascend NPU support ⚡ JIT compilation for CUDA/ROCm kernels on first use ✅ Easier Pip/UV installs with no --no-build-isolation needed 🧠 Models Added: ⚡ GLM 5 / 5.1 👁️ GLM OCR 🎙️ GLM ASR 💎 Gemma 3n 🦅 Falcon Mamba 🖼️ InternVL Chat
Proud to have worked with Nvidia researchers in early 2025 and assisted in implementation and validation of EoRA pipeline in GPT-QModel. It's the real deal! Will be integrating the fused kernel asap.
🚀EoRA accepted to two @iclr_conf 2026 workshops: @ICBINBWorkshop & #TTU! Fine-tuning-free compensation for compressed LLMs using Eigenspace Low-Rank Approximation. Recover up to +11.45% accuracy on 3-bit quantized models — in just minutes with only hundreds of calibration
🥳 Evalution v0.0.7 released with Tinygrad llm (gguf) inference support. You can now pair Evalution with @__tinygrad__ and use the 153+ and growing llm benchmarks (such as GSM8K Platinum) to test for model quality and inference regressions.
Regular Expression go zoom zoom!
PyPcre v0.3.0 is out 🚀 pip install pypcre 🔥 Python bindings for PCRE2 with full feature coverage ⚡ JIT-fast and faster than re and regex on real workloads 🧵 Thread-safe with nogil support
🇫🇷 Evalution révolution!
🥳 Evalution now on PyPi 👐 🧐Evolve your LLMs with better evals. 🇫🇷 Viva la Evalution! Run with Transformers, vLLM, SGLang, TensorRTLLM, GPTQModel & OpenVINO! ✨ 126 benchmark families. 213 CUDA regression specs. Fast, clean, extensible evals. 🚀
Everyone, and we mean everyone benefited greatly from this one man's work. 😍
🦖3 bugs/issues found (one critical) in Transformers Continuous Batching (cb) and FlashAttention2 integration: Wrong cuda graph replay (bad key) causing silent quality collapse. Following PR fixed 2 bugs/issues. The 3rd bug (crash) needs to be fixed separately. @remi_or_ @art_zucker
Lean and fast. The way software is meant to be build. GPT-QModel is now 150x smaller. It will only jit compile the kernels you actually use.
GPT-QModel v6.1.0-dev main branch just landed jit compilation for all bundled kernel. 😤 No more 150MB+ plus 66+ variations of python/cuda/torch wheels! 🥳 Best of all, for the first time, you do not need to pass `--nobuild-isolation` to pypi/uv!
🥳 GPT-QModel v6.0 release is HUGE!
🚀 GPTQModel v6.0.3 is out! ✨ ParoQuant added 🎯 FOEM added 🧠 Gemma4, MiniCPM-O, MiniCPM-V 🧠 GLM4-MoE-Lite 🌿 PrismML/Bonsai inference support Easy install: pip install -U gptqmodel --no-build-isolation 🧵👇
👀Bonsai 1bit model inference support just dropped on GPT-QModel!
🥳@PrismML quantized 1bit Bonsai inference support has been added to GPT-QModel v6.0. In addition, I have evaled the 1bit quant and it is excellent for the bit-size. Score on part with the latest 8B models! Wow. What PrimML did was not an April 1st Joke and is the real deal.
🥳 Official FOEM quantization support added to GPT-QModel main branch.
😍 GPT-QModel just landed official support for FOEM: First-Order Error Matters, Accurate Compensation for Quantized LLM. 👇
ParoQuant implementation in GPT-QModel is now in beta2 phase and getting close to rc quality. There are still variances vs z-lab official but I am closing the gap fast. GPT-QModel will also offer many levels of customizablee ParoQuant control far and beyond what official provides. It's going to be good.
openbmb/MiniCPM-o-4_5 support added to GPT-QModel main branch.
GPTQ-QModel now has world class support for Z-Lab's ParoQuant! Benchmarks to come...
ParoQuant from Z-lab (UCSD) on GPT-QModel is now getting very good results. Quant speed is now much faster than official while maintaining same accuracy. Thanks to Z-Lab staff feedback, we now have clamping support that can maintain accuracy even with high sampling/epochs.
🥳LogBar v0.4.1 patch fix released. Can your py logger do this? Tech demo (unit test) 👇 * Progress bar * Stacked Progress bar * Split regions * Table formatting * Zero dependency and much more...
👀 Our testing showed that brute forcing activations to all expert modules have negative effect (friendly fire) for all experts. GPT-QModel's naive rtn+mad smoothing in comparison shows no such regression for MoE quants while using much less compute during quantization. 👀
🎉 New FailSafe config and FailSafeStrategy, auto enabled by default, to address un-even routing of MoE experts resulting quantization issue of some MoE modules. 👇 Smooth operations are also introduced to FailSafeStrategy to reduce outliers impact... github.com/modelcloud/gpt…
🔥MoE quantization is no longer a calibration nightmare with latest update from GPT-QModel. 👀
🎉 New FailSafe config and FailSafeStrategy, auto enabled by default, to address un-even routing of MoE experts resulting quantization issue of some MoE modules. 👇 Smooth operations are also introduced to FailSafeStrategy to reduce outliers impact... github.com/modelcloud/gpt…
🎆4x faster cpu inference on Intel Xeon contributed by Intel AI just got merged to GPT-QModel. 👇
🚀 Do you want 4x faster cpu inference of gptq quantized models on Intel Xeon? 🤯 Intel just dropped a torch compile compatible patch for GPT-QModel's `torch_fused` cpu kernel. 🤗 @IntelAI github.com/ModelCloud/GPT…
GPT-QModel main branch has fixed AWQ quantization of various MoE models. 👇
🥳GPT-QModel dev update: AWQ MoE quantization fixed. Required the creation of a new 🧲:? (? for fishing hook) capture only notation for my high level model definition language (mdl) implemented as a simple tree. Since v5.2 GPTQ and AWQ, and all other supported quant algos,
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