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metadata
license: other
license_name: qwen-research
license_link: https://huggingface.co/Qwen/Qwen2.5-3B-Instruct/blob/main/LICENSE
language:
  - en
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-3B
tags:
  - chat
library_name: transformers

Qwen2.5-3B-Instruct GGUF Models

Ultra-Low-Bit Quantization with IQ-DynamicGate (1-2 bit)

Our latest quantization method introduces precision-adaptive quantization for ultra-low-bit models (1-2 bit), with benchmark-proven improvements on Llama-3-8B. This approach uses layer-specific strategies to preserve accuracy while maintaining extreme memory efficiency.

Benchmark Context

All tests conducted on Llama-3-8B-Instruct using:

  • Standard perplexity evaluation pipeline
  • 2048-token context window
  • Same prompt set across all quantizations

Method

  • Dynamic Precision Allocation:
    • First/Last 25% of layers β†’ IQ4_XS (selected layers)
    • Middle 50% β†’ IQ2_XXS/IQ3_S (increase efficiency)
  • Critical Component Protection:
    • Embeddings/output layers use Q5_K
    • Reduces error propagation by 38% vs standard 1-2bit

Quantization Performance Comparison (Llama-3-8B)

Quantization Standard PPL DynamicGate PPL Ξ” PPL Std Size DG Size Ξ” Size Std Speed DG Speed
IQ2_XXS 11.30 9.84 -12.9% 2.5G 2.6G +0.1G 234s 246s
IQ2_XS 11.72 11.63 -0.8% 2.7G 2.8G +0.1G 242s 246s
IQ2_S 14.31 9.02 -36.9% 2.7G 2.9G +0.2G 238s 244s
IQ1_M 27.46 15.41 -43.9% 2.2G 2.5G +0.3G 206s 212s
IQ1_S 53.07 32.00 -39.7% 2.1G 2.4G +0.3G 184s 209s

Key:

  • PPL = Perplexity (lower is better)
  • Ξ” PPL = Percentage change from standard to DynamicGate
  • Speed = Inference time (CPU avx2, 2048 token context)
  • Size differences reflect mixed quantization overhead

Key Improvements:

  • πŸ”₯ IQ1_M shows massive 43.9% perplexity reduction (27.46 β†’ 15.41)
  • πŸš€ IQ2_S cuts perplexity by 36.9% while adding only 0.2GB
  • ⚑ IQ1_S maintains 39.7% better accuracy despite 1-bit quantization

Tradeoffs:

  • All variants have modest size increases (0.1-0.3GB)
  • Inference speeds remain comparable (<5% difference)

When to Use These Models

πŸ“Œ Fitting models into GPU VRAM

βœ” Memory-constrained deployments

βœ” Cpu and Edge Devices where 1-2bit errors can be tolerated

βœ” Research into ultra-low-bit quantization

Choosing the Right Model Format

Selecting the correct model format depends on your hardware capabilities and memory constraints.

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.

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

πŸ“Œ 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 high precision but with less of 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.

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

πŸ“Œ 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) β†’ Best for minimal memory usage, may have lower precision.
  • Higher-bit models (Q6_K, Q8_0) β†’ Better accuracy, requires more memory.

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

πŸ“Œ 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

Model Format Precision Memory Usage Device Requirements Best Use Case
BF16 Highest High BF16-supported GPU/CPUs High-speed inference with reduced memory
F16 High High FP16-supported devices GPU inference when BF16 isn't available
Q4_K Medium Low Low CPU or Low-VRAM devices Best for memory-constrained environments
Q6_K Medium Moderate CPU with more memory Better accuracy while still being quantized
Q8_0 High Moderate CPU or GPU with enough VRAM Best accuracy among quantized models
IQ3_XS Very Low Very Low Ultra-low-memory devices Extreme memory efficiency and low accuracy
Q4_0 Low Low ARM or low-memory devices llama.cpp can optimize for ARM devices

Included Files & Details

Qwen2.5-3B-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.

Qwen2.5-3B-Instruct-f16.gguf

  • Model weights stored in F16.
  • Use if your device supports FP16, especially if BF16 is not available.

Qwen2.5-3B-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.

Qwen2.5-3B-Instruct-f16-q8_0.gguf

  • Output & embeddings remain in F16.
  • All other layers quantized to Q8_0.

Qwen2.5-3B-Instruct-q4_k.gguf

  • Output & embeddings quantized to Q8_0.
  • All other layers quantized to Q4_K.
  • Good for CPU inference with limited memory.

Qwen2.5-3B-Instruct-q4_k_s.gguf

  • Smallest Q4_K variant, using less memory at the cost of accuracy.
  • Best for very low-memory setups.

Qwen2.5-3B-Instruct-q6_k.gguf

  • Output & embeddings quantized to Q8_0.
  • All other layers quantized to Q6_K .

Qwen2.5-3B-Instruct-q8_0.gguf

  • Fully Q8 quantized model for better accuracy.
  • Requires more memory but offers higher precision.

Qwen2.5-3B-Instruct-iq3_xs.gguf

  • IQ3_XS quantization, optimized for extreme memory efficiency.
  • Best for ultra-low-memory devices.

Qwen2.5-3B-Instruct-iq3_m.gguf

  • IQ3_M quantization, offering a medium block size for better accuracy.
  • Suitable for low-memory devices.

Qwen2.5-3B-Instruct-q4_0.gguf

  • Pure Q4_0 quantization, optimized for ARM devices.
  • Best for low-memory environments.
  • Prefer IQ4_NL for better accuracy.

πŸš€ If you find these models useful

❀ Please click "Like" if you find this useful!
Help me test my AI-Powered Network Monitor Assistant with quantum-ready security checks:
πŸ‘‰ Free Network Monitor

πŸ’¬ 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):

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

Other Assistants

🟒 TurboLLM – Uses gpt-4-mini for:

πŸ”΅ HugLLM – Open-source models (β‰ˆ8B params):

  • 2x more tokens than TurboLLM
  • AI-powered log analysis
  • 🌐 Runs on Hugging Face Inference API

πŸ’‘ 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"

Qwen2.5-3B-Instruct

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 3B Qwen2.5 model, which has the following features:

  • Type: Causal Language Models
  • Training Stage: Pretraining & Post-training
  • Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings
  • Number of Parameters: 3.09B
  • Number of Paramaters (Non-Embedding): 2.77B
  • Number of Layers: 36
  • Number of Attention Heads (GQA): 16 for Q and 2 for KV
  • Context Length: Full 32,768 tokens and generation 8192 tokens

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'

Quickstart

Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen/Qwen2.5-3B-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]

Evaluation & Performance

Detailed evaluation results are reported in this πŸ“‘ blog.

For requirements on GPU memory and the respective throughput, see results here.

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}
}