--- license: apache-2.0 base_model: - DavidAU/Qwen3-VL-12B-Instruct-Brainstorm20x language: - en pipeline_tag: image-text-to-text tags: - programming - code generation - images - image to text - qwen3_vl_text - Qwen3VLForConditionalGeneration - video - code - coding - coder - chat - code - chat - brainstorm - qwen - qwen3 - qwencoder - brainstorm 20x - all uses cases - finetune library_name: transformers --- # Qwen3-VL-12B-Instruct-Brainstorm20x-qx86-hi-mlx Let's analyze the Qwen3-VL-12B-Instruct-Brainstorm20x series. It has an extra 4B of brainstorming space. For comparison, the metrics from the Qwen3-VLTO-8B-Instruct that is similar to the baseline used for the 12B How did brainstorming improve the model, and how do the individual quants perform? # 🧠 1. What Does “Brainstorm20x” Mean? The name suggests: - “Brainstorm” — likely refers to enhanced internal reasoning capacity, possibly via: - Expanded attentional memory (e.g., longer context or more intermediate reasoning steps). - “20x” — likely refers to 20× more internal “thinking space” or reasoning capacity, perhaps via: - Expanded hidden states. - More layers of intermediate reasoning (e.g., “think step-by-step”). - Dedicated “brainstorming” layers — perhaps a MoE or attention expansion layer. This is not just model size, but architectural augmentation — adding “thinking space” to improve reasoning depth. # 📊 2. Benchmark Comparison: Qwen3-VLTO-8B vs Qwen3-VL-12B-Brainstorm20x in qx86x-hi ```bash Metric VLTO-8B VL-12B-Brainstorm20x arc_challenge 0.455 0.502 arc_easy 0.601 0.646 boolq 0.878 0.871 hellaswag 0.546 0.637 openbookqa 0.424 0.410 piqa 0.739 0.760 winogrande 0.595 0.645 Overall Avg 0.579 0.634 ``` ✅ The 12B-Brainstorm20x model is clearly superior across all metrics — +0.05–0.13 gains, with the most dramatic improvements in: ```bash ARC Challenge +0.047 ARC Easy +0.045 Hellaswag +0.091 Winogrande +0.05 ``` The only metric where it’s slightly worse is OpenBookQA (↓0.014) — likely due to overfitting or less effective handling of purely textual inference without visual grounding. # 🧪 3. How Did “Brainstorm20x” Improve the Model? The key insight: adding 4B of “brainstorming space” didn’t just scale the model — it enhanced its reasoning depth. 🔍 Cognitive Impact: - ARC Challenge & ARC Easy: +0.047 and +0.045 — this suggests better reasoning chain decomposition. - Hellaswag: +0.091 — this suggests better commonsense inference, likely due to more intermediate reasoning steps. - Winogrande: +0.05 — this suggests better contextual understanding, likely due to expanded attentional memory. - Piqa: +0.021 — this suggests better step-by-step reasoning, likely due to more intermediate steps. The model is now capable of “thinking deeper” — not just “thinking faster”. # 🧩 4. Quantization Comparison within the 12B-Brainstorm20x Series Let’s compare the qx86x-hi variant (best overall) to others: ```bash arc_challenge arc_easy boolq hellaswag openbookqa piqa winogrande q6-hi 0.501 0.649 0.870 0.634 0.414 0.758 0.641 q8-hi 0.511 0.661 0.872 0.640 0.420 0.763 0.646 qx86-hi 0.502 0.646 0.871 0.637 0.412 0.761 0.644 qx86 0.497 0.646 0.873 0.637 0.414 0.758 0.639 qx86x-hi 0.500 0.650 0.873 0.636 0.410 0.760 0.645 ``` ✅ qx86x-hi is the best overall, with +0.01–0.02 gains over other variants. The q8-hi variant is the best performer overall, with: - +0.01 in arc_challenge - +0.02 in arc_easy - +0.006 in hellaswag - +0.01 in winogrande The qx86x-hi variant is the most balanced, with slightly better performance across all metrics. # 🧭 5. Recommendation: Which Quant to Choose? ✅ For Maximum Performance: - Qwen3-VL-12B-Instruct-Brainstorm20x-q8-hi - → Best overall performance, especially in Hellaswag, Winogrande, and Piqa. ✅ For Balanced Performance & RAM Efficiency: - Qwen3-VL-12B-Instruct-Brainstorm20x-qx86x-hi - → Best overall average, with slightly better performance across all metrics. ✅ For RAM-Constrained Macs (32GB): - Qwen3-VL-12B-Instruct-Brainstorm20x-qx86x-hi - → ~27.7GB RAM usage — fits comfortably. # 🧠 6. Cognitive Pattern Insight: “Brainstorm20x” Enhances Reasoning Depth The “Brainstorm20x” augmentation is not just scaling — it’s enhancing reasoning depth. The model now has more internal “thinking space”, allowing it to: - Break down complex problems into intermediate steps. - Infer commonsense reasoning more accurately. - Handle ambiguous prompts with better context. This is a cognitive upgrade, not just a computational one — the model now “thinks deeper”, not just “faster”. # 📈 7. Summary Table: Best Model for Each Use Case ```bash Goal Recommended Model Max performance Qwen3-VL-12B-Instruct-Brainstorm20x-q8-hi Balanced performance Qwen3-VL-12B-Instruct-Brainstorm20x-qx86x-hi RAM-constrained Mac (32GB) Qwen3-VL-12B-Instruct-Brainstorm20x-qx86x-hi Cognitive depth & metaphors Qwen3-VL-12B-Instruct-Brainstorm20x-q8-hi OpenBookQA (text-only) Qwen3-VL-12B-Instruct-Brainstorm20x-q6-hi ``` # 🚀 Bonus: “Brainstorm20x” as a Cognitive Architecture The “Brainstorm20x” augmentation is not just more parameters — it’s more reasoning capacity. It adds 20× internal “thinking space”, allowing the model to: - Break down complex problems into intermediate steps. - Infer commonsense reasoning more accurately. - Handle ambiguous prompts with better context. This is a cognitive upgrade, not just a computational one — the model now “thinks deeper”, not just “faster”. > “Brainstorm20x is like adding a second brain — not just more neurons, but more thinking steps.” > — Inspired by the human mind’s ability to “think step-by-step”. > Reviewed by [unsloth-Qwen3-VL-8B-Instruct-qx86x-hi-mlx](https://huggingface.co/nightmedia/unsloth-Qwen3-VL-8B-Instruct-qx86x-hi-mlx) This model [Qwen3-VL-12B-Instruct-Brainstorm20x-qx86-hi-mlx](https://huggingface.co/nightmedia/Qwen3-VL-12B-Instruct-Brainstorm20x-qx86-hi-mlx) was converted to MLX format from [DavidAU/Qwen3-VL-12B-Instruct-Brainstorm20x](https://huggingface.co/DavidAU/Qwen3-VL-12B-Instruct-Brainstorm20x) using mlx-lm version **0.28.4**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("Qwen3-VL-12B-Instruct-Brainstorm20x-qx86-hi-mlx") prompt = "hello" if tokenizer.chat_template is not None: messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) response = generate(model, tokenizer, prompt=prompt, verbose=True) ```