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README.md

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+---
+language:
+- en
+library_name: transformers
+tags:
+- glm
+- MOE
+- pruning
+- compression
+- mlx
+- mlx-my-repo
+license: mit
+name: cerebras/GLM-4.6-REAP-218B-A32B-FP8
+description: 'This model was obtained by uniformly pruning 40% of experts in GLM-4.6-FP8
+  using the REAP method.
+
+  '
+readme: 'https://huggingface.co/cerebras/GLM-4.6-REAP-218B-A32B-FP8/main/README.md
+
+  '
+license_link: https://huggingface.co/zai-org/GLM-4.6-FP8/blob/main/LICENSE
+pipeline_tag: text-generation
+base_model: cerebras/GLM-4.6-REAP-218B-A32B
+---
+
+# Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit
+
+The Model [Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit](https://huggingface.co/Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit) was converted to MLX format from [cerebras/GLM-4.6-REAP-218B-A32B](https://huggingface.co/cerebras/GLM-4.6-REAP-218B-A32B) using mlx-lm version **0.26.4**.
+
+## Use with mlx
+
+```bash
+pip install mlx-lm
+```
+
+```python
+from mlx_lm import load, generate
+
+model, tokenizer = load("Wwayu/GLM-4.6-REAP-218B-A32B-mlx-3Bit")
+
+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)
+```