build: bump gptqmodel from 4.0.0.dev0+cu126torch2.7 to 7.0.0

[dependabot]

Bumps gptqmodel from 4.0.0.dev0+cu126torch2.7 to 7.0.0.

Release notes

Sourced from gptqmodel's releases.

🚀 GPTQModel v7.0.0

🔥 Major

• New Huawei Ascend NPU quantization support with torch based kernels for inference
• All CUDA/ROCm compiled kernels are now JIT (just-in-time) compiled on first use
• Pip/UV install no longer requires the --no-build-isolation flag

🧠 New model support and compatibility wins

• Added support for GLM 5/5.1, GLM OCR, GLM ASR, Gemma 3n, Falcon Mamba, and InternVL Chat.
• Extended OpenVINO GPTQ patching to understand GPTQModel's newer kernels.
• Fixed Qwen3 dtype handling, Qwen3.5 MoE module-tree assertions, Qwen2-VL calibration input capture, and Qwen 3.6 MoE regressions.
• Fixed Llama4Router replacement behavior, Phi-3 defused MLP module mapping, Phi-4 runtime requirements, Instella rope-scaling compatibility, Ling compatibility, Mixtral MoE checkpoint module names, Brumby thread safety, Baichuan compatibility, and Gemma 3 saving.
• Fixed exllamav3_torch import under meta-device context.

⚡ Kernels, JIT, and hardware acceleration

• Moved all compilation required kernels to JIT compilation on first-use and cleaned up Marlin import probing, CUDA header handling, nvcc flag checks, and Torch/CUDA mismatch handling.
• Synced Marlin/Machete kernels with upstream and added hardware-specific Marlin boost paths.
• Guarded CUTLASS version mismatches and fixed generated-kernel staleness.
• Added global kernel rebuild support for CI and safer shared extension locks.
• Added Ascend NPU support.
• Fixed AWQ JIT cache invalidation, illegal memory access, SM120 execution, GEMM_Fast shared-memory launch, and BF16 bias validation.
• Fixed BACKEND.MARLIN loading for gptq_v2 format and added Marlin import coverage.

🔥 Quantization, AWQ, FP8, and dequant

• Added FP8/FP4 CPU dequant and DeepSeek FP8 .scale dequant export.
• Added dtype auto-decoding and decode path updates.
• Reduced AWQ scale-search activation memory and split AWQ integration tests for cleaner coverage.
• Fail fast on unsupported act-group-aware GPTQ shapes instead of continuing into invalid layouts.
• Fixed INT3 qzero format conversion, GAR width compatibility, and GPTQ batched keep-mask handling.
• Improved AWQ W4A8 and BF16 validation paths, plus post-quant MoE routing behavior.
• Used loader device selection for EoRA adapter generation.

🐢 LazyTurtle, loading, and model plumbing

• Refactored input capture into BaseQModel and model-specific QModels for cleaner replay and calibration flows.
• Renamed and hardened the turtle path into LazyTurtle, with stricter materialization failures and better expected-skip handling.
• Fixed LazyTurtle materialization for non-square fused experts, PhiMoE, nested HF weight renames, reversed WeightRenaming semantics, and non-Safetensors checkpoints.
• Improved out-of-model tensor handling for MTP prefix/files paths.
• Removed BaseModel.loader_requires_dtype and normalized config dtype handling through get_hf_config_dtype().
• Fixed multi-GPU replay output retention, GPTQ finalizer overlap, and quantization OOMs from retained callable cache keys.

🧰 CI, packaging, and developer workflow

• Cleaned up CI shell logic, environment setup, UV cache handling, reusable Torch tests, CPU-only grouping, runner selection, retry behavior, and offload temp paths.
• Kept CI and Torch CUDA versions aligned, moved to newer Docker images, and surfaced real exit codes and GPU names.
• Removed lm-eval, deprecated tests, deprecated artifact IDs, pause UI lifecycle code, and tabulate from CI/test paths.

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