gemma-4-12B-it-W4A16-AutoRound
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gemma-4-12B-it-W4A16-AutoRound-AWQ
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View details ↗gemma-4-E2B-it-W4A16-AutoRound
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View details ↗gemma-4-E4B-it-W4A16-AutoRound-GPTQ
Vishva007This repository hosts **W4A16 INT4-quantized** versions of `google/gemma-4-E4B-it`, a multimodal mixture-of-experts model supporting text, vision, and audio inputs. Two quantized variants are available: > **Note on MTP / Speculative Decoding:** > If you want to use the official speculative decoding assistant model (`google/gemma-4-E4B-it-assistant`) for MTP support, it is recommended to use the `vllm/vllm-openai:gemma4-0505-cu129` Docker image, which includes newer Gemma 4 support and decoding patches. > > Due to INT4 quantization, the assistant acceptance rate may be lower compared to the original unquantized `google/gemma-4-E4B-it` model. > **Note:** Only the language model (LM) layers are quantized to INT4. The vision tower, audio tower, and multimodal projectors are kept at full precision (BF16) to preserve multimodal quality.
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View details ↗Qwen3.5-9B-W4A16-AutoRound-AWQ
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View details ↗Qwen3.5-9B-W4A16-AutoRound-GPTQ
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View details ↗Qwen3.8-27B-W4A16-AutoRound
Vishva007This repository contains a **W4A16 (4-bit weights, 16-bit activations)** quantized version of Qwen/Qwen3.8-27B generated using Intel's AutoRound algorithm. The model was calibrated and quantized using production-grade settings to minimize accuracy degradation while significantly lowering VRAM requirements: One-click launch environments pre-configured with PyTorch, CUDA, and dependencies for fine-tuning or quantization.
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View details ↗Qwen3.8-27B-W4A16-AutoRound-GPTQ
Vishva007This repository contains a **W4A16 (4-bit weights, 16-bit activations)** quantized version of Qwen/Qwen3.8-27B generated using Intel's AutoRound algorithm. The model was calibrated and quantized using production-grade settings to minimize accuracy degradation while significantly lowering VRAM requirements: * **Quantization Algorithm:** AutoRound * **Bits / Precision:** W4A16 (4-bit integer weights, 16-bit activation) * **Group Size:** 64 (provides higher reconstruction fidelity than standard 128) * **Symmetric (`sym`):** `True` * **Calibration Samples (`nsamples`):** 512 * **Tuning Iterations (`iters`):** 600 * **Vision Tower (`quant_nontext_module`):** `False` (Kept in **BF16** to preserve visual reasoning and OCR precision) * **Special Modules (`layer_config`):** Multi-Token Prediction (`mtp`, `mtp.fc`) layers preserved in native `bfloat16`.
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View details ↗Qwen3-VL-8B-Instruct-W4A16-AutoRound
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View details ↗This model is the **AWQ (Activation-aware Weight Quantization)** export of Qwen/Qwen3-VL-8B-Instruct. It combines the **speed of AWQ** with the **accuracy of AutoRound**. The weights were fine-tuned for 800 steps to ensure the 4-bit degradation is negligible. The vision tower remains in full precision (FP16) to maintain top-tier performance on OCR and visual tasks. One-click launch environments pre-configured with PyTorch, CUDA, and dependencies for fine-tuning or quantization.
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