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README.md

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+---
+library_name: transformers
+tags:
+- Uncensored
+- Abliterated
+- Cubed Reasoning
+- QwQ-32B
+- reasoning
+- thinking
+- r1
+- cot
+- deepseek
+- Qwen2.5
+- Hermes
+- DeepHermes
+- DeepSeek
+- DeepSeek-R1-Distill
+- 128k context
+- merge
+- mlx
+- mlx-my-repo
+base_model: DavidAU/Qwen2.5-QwQ-35B-Eureka-Cubed-abliterated-uncensored
+---
+
+# bobig/Qwen2.5-QwQ-35B-Eureka-Cubed-abliterated-uncensored-4bit
+
+The Model [bobig/Qwen2.5-QwQ-35B-Eureka-Cubed-abliterated-uncensored-4bit](https://huggingface.co/bobig/Qwen2.5-QwQ-35B-Eureka-Cubed-abliterated-uncensored-4bit) was converted to MLX format from [DavidAU/Qwen2.5-QwQ-35B-Eureka-Cubed-abliterated-uncensored](https://huggingface.co/DavidAU/Qwen2.5-QwQ-35B-Eureka-Cubed-abliterated-uncensored) using mlx-lm version **0.21.5**.
+
+## Use with mlx
+
+```bash
+pip install mlx-lm
+```
+
+```python
+from mlx_lm import load, generate
+
+model, tokenizer = load("bobig/Qwen2.5-QwQ-35B-Eureka-Cubed-abliterated-uncensored-4bit")
+
+prompt="hello"
+
+if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
+    messages = [{"role": "user", "content": prompt}]
+    prompt = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+
+response = generate(model, tokenizer, prompt=prompt, verbose=True)
+```