Qwen2.5 72B Instruct GGUF

🚀 Qwen2.5-72B-Instruct GGUF Models

This project offers Qwen2.5-72B-Instruct GGUF models. It provides various model formats to meet different hardware and memory requirements, and also includes a testing platform for AI network monitoring.

🚀 Quick Start

Choosing the Right Model Format

Selecting the appropriate model format depends on your hardware capabilities and memory constraints. Here are the details of different model formats:

BF16 (Brain Float 16) – Use if BF16 acceleration is available

• A 16-bit floating-point format designed for faster computation while retaining good precision.
• Provides similar dynamic range as FP32 but with lower memory usage.
• Recommended if your hardware supports BF16 acceleration (check your device's specs).
• Ideal for high-performance inference with reduced memory footprint compared to FP32.

📌 Use BF16 if: ✔ Your hardware has native BF16 support (e.g., newer GPUs, TPUs). ✔ You want higher precision while saving memory. ✔ You plan to requantize the model into another format.

📌 Avoid BF16 if: ❌ Your hardware does not support BF16 (it may fall back to FP32 and run slower). ❌ You need compatibility with older devices that lack BF16 optimization.

F16 (Float 16) – More widely supported than BF16

• A 16-bit floating-point format with high precision but a smaller range of values than BF16.
• Works on most devices with FP16 acceleration support (including many GPUs and some CPUs).
• Slightly lower numerical precision than BF16 but generally sufficient for inference.

📌 Use F16 if: ✔ Your hardware supports FP16 but not BF16. ✔ You need a balance between speed, memory usage, and accuracy. ✔ You are running on a GPU or another device optimized for FP16 computations.

📌 Avoid F16 if: ❌ Your device lacks native FP16 support (it may run slower than expected). ❌ You have memory limitations.

Quantized Models (Q4_K, Q6_K, Q8, etc.) – For CPU & Low-VRAM Inference

Quantization reduces model size and memory usage while maintaining as much accuracy as possible.

• Lower-bit models (Q4_K) → Best for minimal memory usage, may have lower precision.
• Higher-bit models (Q6_K, Q8_0) → Better accuracy, requires more memory.

📌 Use Quantized Models if: ✔ You are running inference on a CPU and need an optimized model. ✔ Your device has low VRAM and cannot load full-precision models. ✔ You want to reduce memory footprint while keeping reasonable accuracy.

📌 Avoid Quantized Models if: ❌ You need maximum accuracy (full-precision models are better for this). ❌ Your hardware has enough VRAM for higher-precision formats (BF16/F16).

Very Low-Bit Quantization (IQ3_XS, IQ3_S, IQ3_M, Q4_K, Q4_0)

These models are optimized for extreme memory efficiency, making them ideal for low-power devices or large-scale deployments where memory is a critical constraint.

• IQ3_XS: Ultra-low-bit quantization (3-bit) with extreme memory efficiency.
  • Use case: Best for ultra-low-memory devices where even Q4_K is too large.
  • Trade-off: Lower accuracy compared to higher-bit quantizations.
• IQ3_S: Small block size for maximum memory efficiency.
  • Use case: Best for low-memory devices where IQ3_XS is too aggressive.
• IQ3_M: Medium block size for better accuracy than IQ3_S.
  • Use case: Suitable for low-memory devices where IQ3_S is too limiting.
• Q4_K: 4-bit quantization with block-wise optimization for better accuracy.
  • Use case: Best for low-memory devices where Q6_K is too large.
• Q4_0: Pure 4-bit quantization, optimized for ARM devices.
  • Use case: Best for ARM-based devices or low-memory environments.

Summary Table: Model Format Selection

Included Files & Details

• Qwen2.5-72B-Instruct-bf16.gguf: Model weights preserved in BF16. Use this if you want to requantize the model into a different format. Best if your device supports BF16 acceleration.
• Qwen2.5-72B-Instruct-f16.gguf: Model weights stored in F16. Use if your device supports FP16, especially if BF16 is not available.
• Qwen2.5-72B-Instruct-bf16-q8_0.gguf: Output & embeddings remain in BF16. All other layers quantized to Q8_0. Use if your device supports BF16 and you want a quantized version.
• Qwen2.5-72B-Instruct-f16-q8_0.gguf: Output & embeddings remain in F16. All other layers quantized to Q8_0.
• Qwen2.5-72B-Instruct-q4_k.gguf: Output & embeddings quantized to Q8_0. All other layers quantized to Q4_K. Good for CPU inference with limited memory.
• Qwen2.5-72B-Instruct-q4_k_s.gguf: Smallest Q4_K variant, using less memory at the cost of accuracy. Best for very low-memory setups.
• Qwen2.5-72B-Instruct-q6_k.gguf: Output & embeddings quantized to Q8_0. All other layers quantized to Q6_K.
• Qwen2.5-72B-Instruct-q8_0.gguf: Fully Q8 quantized model for better accuracy. Requires more memory but offers higher precision.
• Qwen2.5-72B-Instruct-iq3_xs.gguf: IQ3_XS quantization, optimized for extreme memory efficiency. Best for ultra-low-memory devices.
• Qwen2.5-72B-Instruct-iq3_m.gguf: IQ3_M quantization, offering a medium block size for better accuracy. Suitable for low-memory devices.
• Qwen2.5-72B-Instruct-q4_0.gguf: Pure Q4_0 quantization, optimized for ARM devices. Best for low-memory environments. Prefer IQ4_NL for better accuracy.

💻 Usage Examples

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen/Qwen2.5-72B-Instruct"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

Advanced Usage

# To handle extensive inputs exceeding 32,768 tokens, add the following to config.json
{
  ...,
  "rope_scaling": {
    "factor": 4.0,
    "original_max_position_embeddings": 32768,
    "type": "yarn"
  }
}

📚 Documentation

Introduction

Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:

• Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains.
• Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.
• Long-context Support up to 128K tokens and can generate up to 8K tokens.
• Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.

This repo contains the instruction-tuned 72B Qwen2.5 model, which has the following features:

• Type: Causal Language Models
• Training Stage: Pretraining & Post-training
• Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
• Number of Parameters: 72.7B
• Number of Paramaters (Non-Embedding): 70.0B
• Number of Layers: 80
• Number of Attention Heads (GQA): 64 for Q and 8 for KV
• Context Length: Full 131,072 tokens and generation 8192 tokens
  • Please refer to this section for detailed instructions on how to deploy Qwen2.5 for handling long texts.

For more details, please refer to our blog, GitHub, and Documentation.

Requirements

The code of Qwen2.5 has been in the latest Hugging face transformers and we advise you to use the latest version of transformers. With transformers<4.37.0, you will encounter the following error:

KeyError: 'qwen2'

Processing Long Texts

The current config.json is set for context length up to 32,768 tokens. To handle extensive inputs exceeding 32,768 tokens, we utilize YaRN, a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For supported frameworks, you could add the following to config.json to enable YaRN:

{
  ...,
  "rope_scaling": {
    "factor": 4.0,
    "original_max_position_embeddings": 32768,
    "type": "yarn"
  }
}

For deployment, we recommend using vLLM. Please refer to our Documentation for usage if you are not familar with vLLM. Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. We advise adding the rope_scaling configuration only when processing long contexts is required.

Evaluation & Performance

Detailed evaluation results are reported in this 📑 blog. For requirements on GPU memory and the respective throughput, see results here.

Testing the Models

If you find these models useful, please click "Like"! Help me test my AI-Powered Network Monitor Assistant with quantum-ready security checks: Free Network Monitor.

How to test

1. Click the chat icon (bottom right on any page).
2. Choose an AI assistant type:
   • TurboLLM (GPT-4-mini)
   • FreeLLM (Open-source)
   • TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring, specifically:

• Function calling against live network services.
• How small can a model go while still handling:
  • Automated Nmap scans.
  • Quantum-readiness checks.
  • Metasploit integration.

TestLLM – Current experimental model (llama.cpp on 6 CPU threads)

• ✅ Zero-configuration setup.
• ⏳ 30s load time (slow inference but no API costs).
• 🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!

Other Assistants

• 🟢 TurboLLM – Uses gpt-4-mini for real-time network diagnostics and automated penetration testing (Nmap/Metasploit). Get more tokens by downloading our Free Network Monitor Agent.
• 🔵 HugLLM – Open-source models (≈8B params). 2x more tokens than TurboLLM, AI-powered log analysis, and runs on Hugging Face Inference API.

Example AI Commands to Test

1. "Give me info on my websites SSL certificate"
2. "Check if my server is using quantum safe encyption for communication"
3. "Run a quick Nmap vulnerability test"

🔧 Technical Details

The architecture of the Qwen2.5-72B-Instruct model is based on transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias. It has 80 layers, 72.7 billion parameters (70.0 billion non-embedding parameters), and supports a context length of up to 131,072 tokens with a generation length of up to 8192 tokens.

📄 License

This project is licensed under the Qwen License.

📖 Citation

If you find our work helpful, feel free to give us a cite.

@misc{qwen2.5,
    title = {Qwen2.5: A Party of Foundation Models},
    url = {https://qwenlm.github.io/blog/qwen2.5/},
    author = {Qwen Team},
    month = {September},
    year = {2024}
}

@article{qwen2,
      title={Qwen2 Technical Report}, 
      author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
      journal={arXiv preprint arXiv:2407.10671},
      year={2024}
}