Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Qwen2.5-Coder-0.5B-Instruct-MLX - GGUF - Model creator: https://huggingface.co/TheBlueObserver/ - Original model: https://huggingface.co/TheBlueObserver/Qwen2.5-Coder-0.5B-Instruct-MLX/ | Name | Quant method | Size | | ---- | ---- | ---- | | [Qwen2.5-Coder-0.5B-Instruct-MLX.Q2_K.gguf](https://huggingface.co/RichardErkhov/TheBlueObserver_-_Qwen2.5-Coder-0.5B-Instruct-MLX-gguf/blob/main/Qwen2.5-Coder-0.5B-Instruct-MLX.Q2_K.gguf) | Q2_K | 0.32GB | | [Qwen2.5-Coder-0.5B-Instruct-MLX.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/TheBlueObserver_-_Qwen2.5-Coder-0.5B-Instruct-MLX-gguf/blob/main/Qwen2.5-Coder-0.5B-Instruct-MLX.IQ3_XS.gguf) | IQ3_XS | 0.32GB | | [Qwen2.5-Coder-0.5B-Instruct-MLX.IQ3_S.gguf](https://huggingface.co/RichardErkhov/TheBlueObserver_-_Qwen2.5-Coder-0.5B-Instruct-MLX-gguf/blob/main/Qwen2.5-Coder-0.5B-Instruct-MLX.IQ3_S.gguf) | IQ3_S | 0.32GB | | [Qwen2.5-Coder-0.5B-Instruct-MLX.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/TheBlueObserver_-_Qwen2.5-Coder-0.5B-Instruct-MLX-gguf/blob/main/Qwen2.5-Coder-0.5B-Instruct-MLX.Q3_K_S.gguf) | Q3_K_S | 0.32GB | | [Qwen2.5-Coder-0.5B-Instruct-MLX.IQ3_M.gguf](https://huggingface.co/RichardErkhov/TheBlueObserver_-_Qwen2.5-Coder-0.5B-Instruct-MLX-gguf/blob/main/Qwen2.5-Coder-0.5B-Instruct-MLX.IQ3_M.gguf) | IQ3_M | 0.32GB | | [Qwen2.5-Coder-0.5B-Instruct-MLX.Q3_K.gguf](https://huggingface.co/RichardErkhov/TheBlueObserver_-_Qwen2.5-Coder-0.5B-Instruct-MLX-gguf/blob/main/Qwen2.5-Coder-0.5B-Instruct-MLX.Q3_K.gguf) | Q3_K | 0.33GB | | [Qwen2.5-Coder-0.5B-Instruct-MLX.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/TheBlueObserver_-_Qwen2.5-Coder-0.5B-Instruct-MLX-gguf/blob/main/Qwen2.5-Coder-0.5B-Instruct-MLX.Q3_K_M.gguf) | Q3_K_M | 0.33GB | | [Qwen2.5-Coder-0.5B-Instruct-MLX.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/TheBlueObserver_-_Qwen2.5-Coder-0.5B-Instruct-MLX-gguf/blob/main/Qwen2.5-Coder-0.5B-Instruct-MLX.Q3_K_L.gguf) | Q3_K_L | 0.34GB | | [Qwen2.5-Coder-0.5B-Instruct-MLX.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/TheBlueObserver_-_Qwen2.5-Coder-0.5B-Instruct-MLX-gguf/blob/main/Qwen2.5-Coder-0.5B-Instruct-MLX.IQ4_XS.gguf) | IQ4_XS | 0.33GB | | [Qwen2.5-Coder-0.5B-Instruct-MLX.Q4_0.gguf](https://huggingface.co/RichardErkhov/TheBlueObserver_-_Qwen2.5-Coder-0.5B-Instruct-MLX-gguf/blob/main/Qwen2.5-Coder-0.5B-Instruct-MLX.Q4_0.gguf) | Q4_0 | 0.33GB | | [Qwen2.5-Coder-0.5B-Instruct-MLX.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/TheBlueObserver_-_Qwen2.5-Coder-0.5B-Instruct-MLX-gguf/blob/main/Qwen2.5-Coder-0.5B-Instruct-MLX.IQ4_NL.gguf) | IQ4_NL | 0.33GB | | [Qwen2.5-Coder-0.5B-Instruct-MLX.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/TheBlueObserver_-_Qwen2.5-Coder-0.5B-Instruct-MLX-gguf/blob/main/Qwen2.5-Coder-0.5B-Instruct-MLX.Q4_K_S.gguf) | Q4_K_S | 0.36GB | | [Qwen2.5-Coder-0.5B-Instruct-MLX.Q4_K.gguf](https://huggingface.co/RichardErkhov/TheBlueObserver_-_Qwen2.5-Coder-0.5B-Instruct-MLX-gguf/blob/main/Qwen2.5-Coder-0.5B-Instruct-MLX.Q4_K.gguf) | Q4_K | 0.37GB | | [Qwen2.5-Coder-0.5B-Instruct-MLX.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/TheBlueObserver_-_Qwen2.5-Coder-0.5B-Instruct-MLX-gguf/blob/main/Qwen2.5-Coder-0.5B-Instruct-MLX.Q4_K_M.gguf) | Q4_K_M | 0.37GB | | [Qwen2.5-Coder-0.5B-Instruct-MLX.Q4_1.gguf](https://huggingface.co/RichardErkhov/TheBlueObserver_-_Qwen2.5-Coder-0.5B-Instruct-MLX-gguf/blob/main/Qwen2.5-Coder-0.5B-Instruct-MLX.Q4_1.gguf) | Q4_1 | 0.35GB | | [Qwen2.5-Coder-0.5B-Instruct-MLX.Q5_0.gguf](https://huggingface.co/RichardErkhov/TheBlueObserver_-_Qwen2.5-Coder-0.5B-Instruct-MLX-gguf/blob/main/Qwen2.5-Coder-0.5B-Instruct-MLX.Q5_0.gguf) | Q5_0 | 0.37GB | | [Qwen2.5-Coder-0.5B-Instruct-MLX.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/TheBlueObserver_-_Qwen2.5-Coder-0.5B-Instruct-MLX-gguf/blob/main/Qwen2.5-Coder-0.5B-Instruct-MLX.Q5_K_S.gguf) | Q5_K_S | 0.38GB | | [Qwen2.5-Coder-0.5B-Instruct-MLX.Q5_K.gguf](https://huggingface.co/RichardErkhov/TheBlueObserver_-_Qwen2.5-Coder-0.5B-Instruct-MLX-gguf/blob/main/Qwen2.5-Coder-0.5B-Instruct-MLX.Q5_K.gguf) | Q5_K | 0.39GB | | [Qwen2.5-Coder-0.5B-Instruct-MLX.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/TheBlueObserver_-_Qwen2.5-Coder-0.5B-Instruct-MLX-gguf/blob/main/Qwen2.5-Coder-0.5B-Instruct-MLX.Q5_K_M.gguf) | Q5_K_M | 0.39GB | | [Qwen2.5-Coder-0.5B-Instruct-MLX.Q5_1.gguf](https://huggingface.co/RichardErkhov/TheBlueObserver_-_Qwen2.5-Coder-0.5B-Instruct-MLX-gguf/blob/main/Qwen2.5-Coder-0.5B-Instruct-MLX.Q5_1.gguf) | Q5_1 | 0.39GB | | [Qwen2.5-Coder-0.5B-Instruct-MLX.Q6_K.gguf](https://huggingface.co/RichardErkhov/TheBlueObserver_-_Qwen2.5-Coder-0.5B-Instruct-MLX-gguf/blob/main/Qwen2.5-Coder-0.5B-Instruct-MLX.Q6_K.gguf) | Q6_K | 0.47GB | | [Qwen2.5-Coder-0.5B-Instruct-MLX.Q8_0.gguf](https://huggingface.co/RichardErkhov/TheBlueObserver_-_Qwen2.5-Coder-0.5B-Instruct-MLX-gguf/blob/main/Qwen2.5-Coder-0.5B-Instruct-MLX.Q8_0.gguf) | Q8_0 | 0.49GB | Original model description: --- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct/blob/main/LICENSE language: - en base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct pipeline_tag: text-generation library_name: transformers tags: - code - codeqwen - chat - qwen - qwen-coder - mlx --- # TheBlueObserver/Qwen2.5-Coder-0.5B-Instruct-MLX The Model [TheBlueObserver/Qwen2.5-Coder-0.5B-Instruct-MLX](https://huggingface.co/TheBlueObserver/Qwen2.5-Coder-0.5B-Instruct-MLX) was converted to MLX format from [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct) using mlx-lm version **0.20.2**. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("TheBlueObserver/Qwen2.5-Coder-0.5B-Instruct-MLX") 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) ```