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  1. README.md +37 -5
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  ---
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  language: en
 
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  library_name: mlx
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  pipeline_tag: text-generation
 
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  tags:
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  - mlx
 
 
 
 
 
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  ---
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- # snsnc/Qwopus3.5-9B-Coder-MLX
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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
@@ -19,13 +30,34 @@ from mlx_lm import load, generate
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  model, tokenizer = load("snsnc/Qwopus3.5-9B-Coder-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, return_dict=False,
 
 
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  )
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  response = generate(model, tokenizer, prompt=prompt, verbose=True)
 
 
 
 
 
 
 
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  ```
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  language: en
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+ license: apache-2.0
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  library_name: mlx
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  pipeline_tag: text-generation
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+ base_model: Jackrong/Qwopus3.5-9B-Coder
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  tags:
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  - mlx
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+ - qwen3_5
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+ - code
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+ - agent
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+ - tool-use
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+ - text-generation
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  ---
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+ # Qwopus3.5-9B-Coder-MLX
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+ MLX conversion of [`Jackrong/Qwopus3.5-9B-Coder`](https://huggingface.co/Jackrong/Qwopus3.5-9B-Coder) for Apple Silicon.
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+
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+ Original upstream weights, converted to MLX format only. No additional fine-tuning or architecture changes.
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+
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+ ## Use with mlx-lm
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  ```bash
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+ pip install -U mlx-lm
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  ```
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  ```python
 
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  model, tokenizer = load("snsnc/Qwopus3.5-9B-Coder-MLX")
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+ prompt = "Write a short Python function that returns the factorial of a number."
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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,
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+ add_generation_prompt=True,
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+ return_dict=False,
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  )
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  response = generate(model, tokenizer, prompt=prompt, verbose=True)
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+ print(response)
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+ ```
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+
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+ ## Use with mlx-serve
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+
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+ ```bash
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+ pip install -U mlx-serve
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  ```
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+
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+ ```bash
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+ mlx-serve \
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+ --model hf://snsnc/Qwopus3.5-9B-Coder-MLX \
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+ --serve \
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+ --host 0.0.0.0 \
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+ --port 11234
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+ ```
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+
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+ ## Upstream
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+
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+ - Original model: [`Jackrong/Qwopus3.5-9B-Coder`](https://huggingface.co/Jackrong/Qwopus3.5-9B-Coder)