Name	Quant method	Size
Qwen2.5-32B-Instruct.Q2_K.gguf	Q2_K	11.47GB
Qwen2.5-32B-Instruct.IQ3_XS.gguf	IQ3_XS	12.76GB
Qwen2.5-32B-Instruct.IQ3_S.gguf	IQ3_S	13.45GB
Qwen2.5-32B-Instruct.Q3_K_S.gguf	Q3_K_S	13.4GB
Qwen2.5-32B-Instruct.IQ3_M.gguf	IQ3_M	13.79GB
Qwen2.5-32B-Instruct.Q3_K.gguf	Q3_K	14.84GB
Qwen2.5-32B-Instruct.Q3_K_M.gguf	Q3_K_M	14.84GB
Qwen2.5-32B-Instruct.Q3_K_L.gguf	Q3_K_L	16.06GB
Qwen2.5-32B-Instruct.IQ4_XS.gguf	IQ4_XS	16.64GB
Qwen2.5-32B-Instruct.Q4_0.gguf	Q4_0	17.36GB
Qwen2.5-32B-Instruct.IQ4_NL.gguf	IQ4_NL	17.53GB
Qwen2.5-32B-Instruct.Q4_K_S.gguf	Q4_K_S	17.49GB
Qwen2.5-32B-Instruct.Q4_K.gguf	Q4_K	18.49GB
Qwen2.5-32B-Instruct.Q4_K_M.gguf	Q4_K_M	18.49GB
Qwen2.5-32B-Instruct.Q4_1.gguf	Q4_1	19.22GB
Qwen2.5-32B-Instruct.Q5_0.gguf	Q5_0	21.08GB
Qwen2.5-32B-Instruct.Q5_K_S.gguf	Q5_K_S	21.08GB
Qwen2.5-32B-Instruct.Q5_K.gguf	Q5_K	21.66GB
Qwen2.5-32B-Instruct.Q5_K_M.gguf	Q5_K_M	21.66GB
Qwen2.5-32B-Instruct.Q5_1.gguf	Q5_1	22.95GB
Qwen2.5-32B-Instruct.Q6_K.gguf	Q6_K	25.04GB
Qwen2.5-32B-Instruct.Q8_0.gguf	Q8_0	32.43GB

Original model description:

base_model: Qwen/Qwen2.5-32B-Instruct language:

en library_name: transformers license: apache-2.0 tags:
unsloth
transformers

Finetune Llama 3.1, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!

We have a Qwen 2.5 (all model sizes) free Google Colab Tesla T4 notebook. Also a Qwen 2.5 conversational style notebook.

✨ Finetune for Free

All notebooks are beginner friendly! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.

Unsloth supports	Free Notebooks	Performance	Memory use
Llama-3.1 8b	▶️ Start on Colab	2.4x faster	58% less
Phi-3.5 (mini)	▶️ Start on Colab	2x faster	50% less
Gemma-2 9b	▶️ Start on Colab	2.4x faster	58% less
Mistral 7b	▶️ Start on Colab	2.2x faster	62% less
TinyLlama	▶️ Start on Colab	3.9x faster	74% less
DPO - Zephyr	▶️ Start on Colab	1.9x faster	19% less

This conversational notebook is useful for ShareGPT ChatML / Vicuna templates.
This text completion notebook is for raw text. This DPO notebook replicates Zephyr.
* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.

Qwen2.5-32B-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 32B 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: 32.5B
Number of Paramaters (Non-Embedding): 31.0B
Number of Layers: 64
Number of Attention Heads (GQA): 40 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'

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.

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Qwen/Qwen2.5-32B-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]

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:

json

{
  ...,
  "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.

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}
}

RichardErkhov/unsloth_-_Qwen2.5-32B-Instruct-gguf