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README.md
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<div align="center">
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# [MLX] Qwen2.5-7B-Instruct-MLX
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**Qwen/Qwen2.5-7B-Instruct** converted to **MLX** format
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[](https://github.com/codewithdark-git/QuantLLM)
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[]()
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[]()
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<a href="https://github.com/codewithdark-git/QuantLLM">[STAR] Star QuantLLM on GitHub</a>
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</div>
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---
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## [ABOUT] About This Model
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This model is **[Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)** converted to **MLX** format optimized for Apple Silicon (M1/M2/M3/M4) Macs with native acceleration.
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| Property | Value |
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|----------|-------|
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| **Base Model** | [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) |
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| **Format** | MLX |
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| **Quantization** | 4bit |
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| **License** | apache-2.0 |
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| **Created With** | [QuantLLM](https://github.com/codewithdark-git/QuantLLM) |
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## [START] Quick Start
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### Generate Text with mlx-lm
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```python
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from mlx_lm import load, generate
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# Load the model
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model, tokenizer = load("QuantLLM/Qwen2.5-7B-Instruct-MLX")
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# Simple generation
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prompt = "Explain quantum computing in simple terms"
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messages = [{"role": "user", "content": prompt}]
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prompt_formatted = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True
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)
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# Generate response
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text = generate(model, tokenizer, prompt=prompt_formatted, verbose=True)
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print(text)
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```
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### Streaming Generation
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```python
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from mlx_lm import load, stream_generate
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model, tokenizer = load("QuantLLM/Qwen2.5-7B-Instruct-MLX")
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prompt = "Write a haiku about coding"
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messages = [{"role": "user", "content": prompt}]
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prompt_formatted = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True
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)
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# Stream tokens as they're generated
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for token in stream_generate(model, tokenizer, prompt=prompt_formatted, max_tokens=200):
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print(token, end="", flush=True)
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```
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### Command Line Interface
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```bash
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# Install mlx-lm
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pip install mlx-lm
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# Generate text
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python -m mlx_lm.generate --model QuantLLM/Qwen2.5-7B-Instruct-MLX --prompt "Hello!"
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# Interactive chat
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python -m mlx_lm.chat --model QuantLLM/Qwen2.5-7B-Instruct-MLX
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```
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### System Requirements
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| Requirement | Minimum |
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|-------------|---------|
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| **Chip** | Apple Silicon (M1/M2/M3/M4) |
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| **macOS** | 13.0 (Ventura) or later |
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| **Python** | 3.10+ |
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| **RAM** | 8GB+ (16GB recommended) |
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```bash
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# Install dependencies
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pip install mlx-lm
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```
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## [DETAILS] Model Details
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| Property | Value |
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|----------|-------|
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| **Original Model** | [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) |
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| **Format** | MLX |
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| **Quantization** | 4bit |
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| **License** | `apache-2.0` |
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| **Export Date** | 2026-08-03 |
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| **Exported By** | [QuantLLM v2.1](https://github.com/codewithdark-git/QuantLLM) |
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---
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## [START] Created with QuantLLM
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<div align="center">
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[](https://github.com/codewithdark-git/QuantLLM)
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**Convert any model to GGUF, ONNX, or MLX in one line!**
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```python
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from quantllm import turbo
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# Load any HuggingFace model
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model = turbo("Qwen/Qwen2.5-7B-Instruct")
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# Export to any format
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model.export("mlx", quantization="4bit")
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# Push to HuggingFace
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model.push("your-repo", format="mlx")
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```
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<a href="https://github.com/codewithdark-git/QuantLLM">
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<img src="https://img.shields.io/github/stars/codewithdark-git/QuantLLM?style=social" alt="GitHub Stars">
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</a>
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**[DOCS](https://github.com/codewithdark-git/QuantLLM#readme)** ·
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**[ISSUE](https://github.com/codewithdark-git/QuantLLM/issues)** ·
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**[FEATURE](https://github.com/codewithdark-git/QuantLLM/issues)**
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</div>
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{
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"tags": [
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"qwen"
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],
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"params": 7.0,
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"quality_score": 4.0,
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"speed_score": 4.5,
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"verified": true,
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"recommended_quant": "4bit"
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}
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---
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# QuantLLM/Qwen2.5-7B-Instruct-MLX
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QuantLLM-registered model.
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