Name	Quant method	Size
Qwen2.5-Coder-1.5B.Q2_K.gguf	Q2_K	0.63GB
Qwen2.5-Coder-1.5B.IQ3_XS.gguf	IQ3_XS	0.68GB
Qwen2.5-Coder-1.5B.IQ3_S.gguf	IQ3_S	0.71GB
Qwen2.5-Coder-1.5B.Q3_K_S.gguf	Q3_K_S	0.71GB
Qwen2.5-Coder-1.5B.IQ3_M.gguf	IQ3_M	0.72GB
Qwen2.5-Coder-1.5B.Q3_K.gguf	Q3_K	0.77GB
Qwen2.5-Coder-1.5B.Q3_K_M.gguf	Q3_K_M	0.77GB
Qwen2.5-Coder-1.5B.Q3_K_L.gguf	Q3_K_L	0.82GB
Qwen2.5-Coder-1.5B.IQ4_XS.gguf	IQ4_XS	0.84GB
Qwen2.5-Coder-1.5B.Q4_0.gguf	Q4_0	0.87GB
Qwen2.5-Coder-1.5B.IQ4_NL.gguf	IQ4_NL	0.88GB
Qwen2.5-Coder-1.5B.Q4_K_S.gguf	Q4_K_S	0.88GB
Qwen2.5-Coder-1.5B.Q4_K.gguf	Q4_K	0.92GB
Qwen2.5-Coder-1.5B.Q4_K_M.gguf	Q4_K_M	0.92GB
Qwen2.5-Coder-1.5B.Q4_1.gguf	Q4_1	0.95GB
Qwen2.5-Coder-1.5B.Q5_0.gguf	Q5_0	1.02GB
Qwen2.5-Coder-1.5B.Q5_K_S.gguf	Q5_K_S	1.02GB
Qwen2.5-Coder-1.5B.Q5_K.gguf	Q5_K	1.05GB
Qwen2.5-Coder-1.5B.Q5_K_M.gguf	Q5_K_M	1.05GB
Qwen2.5-Coder-1.5B.Q5_1.gguf	Q5_1	1.1GB
Qwen2.5-Coder-1.5B.Q6_K.gguf	Q6_K	1.19GB
Qwen2.5-Coder-1.5B.Q8_0.gguf	Q8_0	1.53GB

Original model description:

license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B/blob/main/LICENSE language:

en base_model:
Qwen/Qwen2.5-1.5B pipeline_tag: text-generation library_name: transformers tags:
code
qwen
qwen-coder
codeqwen

Qwen2.5-Coder-1.5B

Introduction

Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). For Qwen2.5-Coder, we release three base language models and instruction-tuned language models, 1.5, 7 and 32 (coming soon) billion parameters. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5:

Significantly improvements in code generation, code reasoning and code fixing. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc.
A more comprehensive foundation for real-world applications such as Code Agents. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies.
Long-context Support up to 128K tokens.

This repo contains the 1.5B Qwen2.5-Coder model, which has the following features:

Type: Causal Language Models
Training Stage: Pretraining
Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings
Number of Parameters: 1.54B
Number of Paramaters (Non-Embedding): 1.31B
Number of Layers: 28
Number of Attention Heads (GQA): 12 for Q and 2 for KV
Context Length: Full 131,072 tokens
- Please refer to this section for detailed instructions on how to deploy Qwen2.5 for handling long texts.

We do not recommend using base language models for conversations. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., or fill in the middle tasks on this model.

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

Requirements

The code of Qwen2.5-Coder 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:

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.

@article{hui2024qwen2,
  title={Qwen2. 5-Coder Technical Report},
  author={Hui, Binyuan and Yang, Jian and Cui, Zeyu and Yang, Jiaxi and Liu, Dayiheng and Zhang, Lei and Liu, Tianyu and Zhang, Jiajun and Yu, Bowen and Dang, Kai and others},
  journal={arXiv preprint arXiv:2409.12186},
  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/Qwen_-_Qwen2.5-Coder-1.5B-gguf