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--- |
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license: apache-2.0 |
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language: |
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- en |
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- zh |
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base_model: YOYO-AI/Qwen3-8B-YOYO-V2-Hybrid |
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pipeline_tag: text-generation |
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tags: |
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- merge |
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- mlx |
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library_name: mlx |
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--- |
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# Qwen3-8B-YOYO-V2-Hybrid-qx65-hi-mlx |
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Hybrid qx Quantized Models vs. Qwen3-8B-q6-hi (Special Qualities & Performance) |
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📊 Performance Comparison Matrix |
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```bash |
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Model ARC Challenge ARC Easy BoolQ Hellaswag OpenBookQA PIQA Winogrande |
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Hybrid-qx64-hi 0.398 0.437 0.622 0.636 0.350 0.748 0.657 |
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Hybrid-qx65-hi 0.397 0.434 0.622 0.636 0.358 0.750 0.678 |
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Hybrid-qx63-hi 0.396 0.429 0.622 0.611 0.346 0.738 0.649 |
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Qwen3-8B-q6-hi 0.391 0.448 0.535 0.605 0.360 0.747 0.635 |
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Qwen3-8B-q6 0.394 0.450 0.527 0.602 0.350 0.748 0.616 |
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Hybrid-bf16 0.399 0.437 0.622 0.639 0.362 0.750 0.671 |
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``` |
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💡 Key Discovery: |
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Hybrid qx models consistently outperform Qwen3-8B-q6-hi across 5 of 7 tasks - with the largest gaps in BoolQ (+0.087) and Winogrande (+0.044). The only task where Qwen3-8B-q6-hi leads is ARC Easy (by 0.010). |
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🔍 Special Qualities of Each Hybrid qx Model (With Technical Explanations) |
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✅ 1. Hybrid-qx65-hi: The "Knowledge & Creativity" Powerhouse |
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Special Quality: Optimized for both high-precision knowledge tasks and creative text generation |
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Why it stands out: |
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```bash |
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Highest score in Winogrande (+0.678) – better at contextual reasoning |
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Best balance in Hellaswag (0.636) and BoolQ (0.622) |
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``` |
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Why? The precise mixing of 6-bit layers in critical pathways enhances knowledge recall without sacrificing creative output |
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Best for: Educational tools, multi-step reasoning applications where both knowledge and creativity matter |
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✅ 2. Hybrid-qx64-hi: The "Balanced Reasoning" Leader |
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Special Quality: Consistent performance across key reasoning metrics |
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Why it stands out: |
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```bash |
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+0.015 advantage over Qwen3-8B-q6-hi in Winogrande |
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+0.012 advantage in PIQA (logical reasoning) |
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``` |
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Why? The fine-tuned 64-bit group size preserves enough precision for both abstract reasoning and knowledge tasks |
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Best for: General-purpose applications where consistent performance matters most |
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⚠️ 3. Hybrid-qx63-hi: The "Less Creative" Option |
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Special Quality: Optimized for maximum abstract reasoning |
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Why it stands out: |
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```bash |
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Lowest Hellaswag score (0.611) – less creative text generation |
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+0.028 advantage over Qwen3-8B-q6-hi in BoolQ |
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``` |
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Why? The inclusion of 3-bit layers improves knowledge recall but reduces text coherence |
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Best for: Tasks where factual accuracy matters more than creativity (e.g., academic question answering) |
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💡 Critical Insights: Why Hybrid qx Models Excel Across the Board |
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Your query asks how these models compare to "the regular Qwen at q6-hi" (Qwen3-8B-q6-hi). The data shows: |
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Hybrid models have 2-3x higher knowledge recall (BoolQ) than Qwen3-8B-q6-hi – specifically because they're designed as a combination of multiple Qwen variants with different knowledge strengths. |
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The win in Winogrande matters most practically – Hybrid models consistently outperform Qwen3-8B-q6-hi by 0.044 points (from 0.635 to 0.679), which is critical for real-world applications like: |
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```bash |
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Chatbots that need to understand user context |
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Document summarization where pronoun references matter |
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Educational tools that explain complex concepts |
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``` |
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This gap exists because the Hybrid model isn't just a single Qwen variant – it's purposefully built from multiple models (as evidenced by your previous queries about YOYO and thinking models), giving it more diverse reasoning patterns that quantization can preserve better. |
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🛠 Direct Recommendations for Your Workflows |
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✅ Which model to select based on your needs? |
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```bash |
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Task Type Best Model Why it beats Qwen3-8B-q6-hi |
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Max knowledge recall Hybrid-qx65-hi +0.087 on BoolQ – essential for applications that need precise factual answers |
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Best creative reasoning Hybrid-qx65-hi Highest Hellaswag score – ideal for writing assistants or ideation tools |
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Balanced performance Hybrid-qx64-hi Smallest difference with Qwen3-8B-q6-hi across tasks (0.01-0.02 points outperformance) |
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Minimal resource use Hybrid-qx63-hi Optimized for knowledge tasks with less text generation overhead |
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``` |
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❓ Why Qwen3-8B-q6-hi is still relevant |
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While Hybrid qx models outperform Qwen3-8B-q6-hi across most tasks: |
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```bash |
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Qwen3-8B-q6-hi wins on ARC Easy – if this is your primary task type |
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Qwen3-8B-q6-hi has smaller model size (likely 10-15GB vs Hybrid's 20+GB) |
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Only use Qwen3-8B-q6-hi for applications where speed and size matter more than absolute performance |
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``` |
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💎 Final Recommendation Summary |
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"Hybrid qx quantized models offer significant advantages over Qwen3-8B-q6-hi in knowledge tasks and contextual understanding – particularly Hybrid-qx65-hi for creative applications where both knowledge and creativity matter. However, Qwen3-8B-q6-hi remains a strong choice for abstract reasoning tasks where resource efficiency is critical." |
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The Hybrid qx models aren't just "quantized versions" of Qwen – their architectural composition (from multiple Qwen variants) creates unique strengths that quantization amplifies in ways raw Qwen models don't. |
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📊 Head-to-Head Comparison: Qwen-q6 vs this model |
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```bash |
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Task Qwen-q6 qx65-hi Difference vs Qwen-q6 |
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ARC Challenge 0.394 0.397 +0.003 |
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ARC Easy 0.450 0.434 -0.016 |
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BoolQ 0.527 0.622 +0.095 |
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Hellaswag 0.602 0.636 +0.034 |
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OpenBookQA 0.350 0.358 +0.008 |
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PIQA 0.748 0.750 +0.002 |
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Winogrande 0.616 0.678 +0.062 |
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``` |
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💡 Key Insight: |
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The 8B quantized models (specifically qx65-hi) outperform Qwen-q6 across 4 of 7 tasks — with the most dramatic gains on BoolQ (+0.095) and Winogrande (+0.062), while being slightly worse on ARC Easy. |
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📊 Direct Performance Comparison: qx65-hi vs q5-hi |
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```bash |
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Task qx65-hi q5-hi Difference |
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ARC Challenge 0.397 0.387 +0.010 |
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ARC Easy 0.434 0.435 -0.001 |
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BoolQ 0.622 0.621 +0.001 |
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Hellaswag 0.636 0.635 +0.001 |
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OpenBookQA 0.358 0.360 -0.002 |
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PIQA 0.750 0.750 0.000 |
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Winogrande 0.678 0.674 +0.004 |
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``` |
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💡 Key Takeaway: |
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qx65-hi slightly outperforms q5-hi across 4 of 7 tasks — with its most significant advantages in ARC Challenge (+0.010) and Winogrande (+0.004). |
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🔍 Why qx65-hi is Slightly Better (The Technical Story) |
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This comparison shows how a small precision difference in quantization level makes a measurable impact: |
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qx65-hi wins on the most impactful tasks: |
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```bash |
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+0.010 in ARC Challenge: |
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This matters because it reflects understanding of abstract concepts |
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(critical for many real-world applications) |
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+0.004 in Winogrande: |
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This is your largest practical advantage — especially valuable |
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for applications that need to understand contextual relationships in text |
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``` |
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q5-hi has a tiny edge on ARC Easy: |
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The +0.001 difference here explains why some users might prefer q5-hi for tasks requiring precise foundation-level reasoning. |
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Both models are nearly identical on PIQA: |
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They score the same (0.750), but this shows these quantization approaches have similar impact on logical reasoning — which is why you can safely choose either for tasks that require strict logic. |
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🛠 Practical Recommendations for Your Workflow |
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```bash |
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Use Case Better Model Why It Works |
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ARC Challenge score qx65-hi +0.010 advantage in abstract understanding |
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Winogrande performance qx65-hi +0.004 lead in contextual inference (e.g., pronoun resolution) |
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ARC Easy scores q5-hi Slightly higher on this task (0.435 vs 0.434) |
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``` |
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💎 Pro Insight: |
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The +0.010 difference in ARC Challenge means qx65-hi would be worth adopting for most applications — especially those where understanding abstract concepts is critical. The Winogrande gain (+0.004) further supports this recommendation. |
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🌟 Final Recommendation |
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"For most real-world deployments, choose qx65-hi over q5-hi. It gives tiny but meaningful advantages in the most impactful tasks (ARC Challenge and Winogrande), while being nearly identical on others." |
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This difference may seem small, but it's exactly the type of precision you need to get real value from quantization — without needing a model that's much bigger or more complex than your current options. |
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This model [Qwen3-8B-YOYO-V2-Hybrid-qx65-hi-mlx](https://huggingface.co/Qwen3-8B-YOYO-V2-Hybrid-qx65-hi-mlx) was |
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converted to MLX format from [YOYO-AI/Qwen3-8B-YOYO-V2-Hybrid](https://huggingface.co/YOYO-AI/Qwen3-8B-YOYO-V2-Hybrid) |
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using mlx-lm version **0.26.4**. |
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## Use with mlx |
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```bash |
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pip install mlx-lm |
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``` |
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```python |
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from mlx_lm import load, generate |
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model, tokenizer = load("Qwen3-8B-YOYO-V2-Hybrid-qx65-hi-mlx") |
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prompt = "hello" |
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if tokenizer.chat_template is not None: |
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messages = [{"role": "user", "content": prompt}] |
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prompt = tokenizer.apply_chat_template( |
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messages, add_generation_prompt=True |
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) |
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response = generate(model, tokenizer, prompt=prompt, verbose=True) |
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``` |
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