๐ฆReddit r/LocalLLaMAโขFreshcollected in 13h
Fixed Qwen 35B GGUF Beats Tensor Drift
๐กNew fix makes 35B Qwen stable for local long-context useโbeats quantization bugs in SSM layers
โก 30-Second TL;DR
What Changed
Fixes ssm_conv1d.weight tensor drift in blocks 36-38 via Wasserstein W1 metric
Why It Matters
Enables reliable local inference of large Qwen models on consumer hardware by addressing quantization bugs, potentially benefiting open-source LLM deployment.
What To Do Next
Download Qwen3.6-35B-A3B-Uncensored-Wasserstein-GGUF from Hugging Face and test in LM Studio with temp=0.7.
Who should care:Developers & AI Engineers
๐ง Deep Insight
AI-generated analysis for this event.
๐ Enhanced Key Takeaways
- โขThe Wasserstein-1 (Earth Mover's Distance) metric is being increasingly adopted in quantization research to address non-linear weight distribution shifts that traditional KL divergence fails to capture in State Space Model (SSM) architectures.
- โขThe 'tensor drift' issue specifically in Qwen-35B's ssm_conv1d layers is linked to the sensitivity of the underlying Mamba-style selective scan mechanism when subjected to low-bit integer quantization.
- โขCommunity-driven 'uncensored' variants of Qwen models often utilize fine-tuning datasets stripped of RLHF-based refusal mechanisms, which can inadvertently degrade the model's adherence to safety-aligned system prompts during complex roleplay.
๐ Competitor Analysisโธ Show
| Feature | Qwen-35B (Fixed GGUF) | Llama-3-70B (GGUF) | Mistral-Large-2 (GGUF) |
|---|---|---|---|
| Architecture | Hybrid Transformer/SSM | Dense Transformer | Dense Transformer |
| Quantization Stability | High (W1-optimized) | Standard (KL-optimized) | Standard (KL-optimized) |
| VRAM Efficiency | High (35B parameter) | Moderate (70B parameter) | Moderate (123B parameter) |
| Primary Use Case | Long-context/Roleplay | General Purpose | Reasoning/Coding |
๐ ๏ธ Technical Deep Dive
- The ssm_conv1d layer in Qwen-35B utilizes a 1D convolution over the channel dimension, which acts as a feature extractor before the selective state space transition.
- Wasserstein distance (W1) optimization minimizes the cost of transforming the distribution of quantized weights to match the original FP16 distribution, preventing the 'drift' that causes numerical instability in the recurrent state.
- The Q4_K_P quantization format employs a specific block-wise scaling strategy that preserves the dynamic range of the convolution weights more effectively than standard Q4_K_M.
๐ฎ Future ImplicationsAI analysis grounded in cited sources
W1-based quantization will become the industry standard for SSM-based LLMs.
Traditional KL divergence metrics are mathematically insufficient for the specific numerical sensitivities found in recurrent state space architectures.
Automated quantization pipelines will integrate Wasserstein metrics by Q4 2026.
The success of manual fixes for tensor drift in Qwen-35B demonstrates a clear performance benefit that can be generalized into automated model conversion tools.
โณ Timeline
2025-09
Alibaba releases Qwen-3.6 series, introducing hybrid SSM-Transformer architecture.
2026-02
Community reports numerical instability and 'tensor drift' in quantized Qwen-35B SSM layers.
2026-04
Release of W1-optimized GGUF quantization for Qwen-35B.
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Original source: Reddit r/LocalLLaMA โ