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---
library_name: mlx
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- mlx
base_model: Qwen/Qwen3-30B-A3B-Thinking-2507
---
# Qwen3-30B-A3B-Thinking-2507-512k-qx6-mlx
this model uses an experimental quanting combination
code name: Deckard
purpose: evaluating replicants
Analysis of qx6 Performance:
Best Suited Tasks for qx6:
1. OpenBookQA (0.432)
This is the highest score among all models in this dataset
+0.002 improvement over bf16 (0.430)
Strongest performance for knowledge-based reasoning tasks
2. BoolQ (0.881)
Highest among all quantized models for boolean reasoning
Only 0.002 behind baseline (0.879)
Excellent for logical reasoning and question answering
3. Arc_Challenge (0.422)
Perfect match with baseline (0.422)
Maintains full performance on the most challenging questions
Secondary Strengths:
4. PIQA (0.724)
Above baseline performance (0.720)
Strong physical interaction reasoning
5. HellaSwag (0.546)
Very close to baseline (0.550)
Good commonsense reasoning
Key Advantages:
Best overall performance in OpenBookQA (0.432)
Perfect retention of Arc_Challenge performance
Exceptional BoolQ scores
Strong knowledge reasoning capabilities
Recommendation:
qx6 is best suited for OpenBookQA and BoolQ tasks.
The model's exceptional performance in OpenBookQA (highest among all models) combined with its perfect retention of Arc_Challenge and superior BoolQ scores makes it ideal for:
Knowledge-intensive question answering systems
Educational assessment applications
Logical reasoning tasks requiring factual accuracy
Research and academic question answering
The model demonstrates optimal balance between knowledge retention and logical processing, making it particularly valuable for applications where both factual recall and reasoning skills are crucial.
This model [Qwen3-30B-A3B-Thinking-2507-512k-qx6-mlx](https://huggingface.co/Qwen3-30B-A3B-Thinking-2507-512k-qx6-mlx) was
converted to MLX format from [Qwen/Qwen3-30B-A3B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507)
using mlx-lm version **0.26.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("Qwen3-30B-A3B-Thinking-2507-512k-qx6-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)
```
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