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+ Qwen RESEARCH LICENSE AGREEMENT
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+
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+ Qwen RESEARCH LICENSE AGREEMENT Release Date: September 19, 2024
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+
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+ By clicking to agree or by using or distributing any portion or element of the Qwen Materials, you will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately.
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+
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+ 1. Definitions
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+ a. This Qwen RESEARCH LICENSE AGREEMENT (this "Agreement") shall mean the terms and conditions for use, reproduction, distribution and modification of the Materials as defined by this Agreement.
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+ b. "We" (or "Us") shall mean Alibaba Cloud.
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+ c. "You" (or "Your") shall mean a natural person or legal entity exercising the rights granted by this Agreement and/or using the Materials for any purpose and in any field of use.
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+ e. "Qwen" shall mean the large language models, and software and algorithms, consisting of trained model weights, parameters (including optimizer states), machine-learning model code, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by us.
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+ f. "Materials" shall mean, collectively, Alibaba Cloud's proprietary Qwen and Documentation (and any portion thereof) made available under this Agreement.
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+ i. "Non-Commercial" shall mean for research or evaluation purposes only.
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+ 2. Grant of Rights
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+ b. If you are commercially using the Materials, you shall request a license from us.
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+ 3. Redistribution
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+ 4. Rules of use
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+ a. The Materials may be subject to export controls or restrictions in China, the United States or other countries or regions. You shall comply with applicable laws and regulations in your use of the Materials.
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+ b. If you use the Materials or any outputs or results therefrom to create, train, fine-tune, or improve an AI model that is distributed or made available, you shall prominently display “Built with Qwen” or “Improved using Qwen” in the related product documentation.
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+ 5. Intellectual Property
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+ a. We retain ownership of all intellectual property rights in and to the Materials and derivatives made by or for us. Conditioned upon compliance with the terms and conditions of this Agreement, with respect to any derivative works and modifications of the Materials that are made by you, you are and will be the owner of such derivative works and modifications.
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+ b. No trademark license is granted to use the trade names, trademarks, service marks, or product names of us, except as required to fulfill notice requirements under this Agreement or as required for reasonable and customary use in describing and redistributing the Materials.
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+ c. If you commence a lawsuit or other proceedings (including a cross-claim or counterclaim in a lawsuit) against us or any entity alleging that the Materials or any output therefrom, or any part of the foregoing, infringe any intellectual property or other right owned or licensable by you, then all licenses granted to you under this Agreement shall terminate as of the date such lawsuit or other proceeding is commenced or brought.
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+ 6. Disclaimer of Warranty and Limitation of Liability
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+ a. We are not obligated to support, update, provide training for, or develop any further version of the Qwen Materials or to grant any license thereto.
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+ c. IN NO EVENT SHALL WE BE LIABLE TO YOU FOR ANY DAMAGES, INCLUDING, BUT NOT LIMITED TO ANY DIRECT, OR INDIRECT, SPECIAL OR CONSEQUENTIAL DAMAGES ARISING FROM YOUR USE OR INABILITY TO USE THE MATERIALS OR ANY OUTPUT OF IT, NO MATTER HOW IT’S CAUSED.
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+ 7. Survival and Termination.
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+ a. The term of this Agreement shall commence upon your acceptance of this Agreement or access to the Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein.
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+ b. We may terminate this Agreement if you breach any of the terms or conditions of this Agreement. Upon termination of this Agreement, you must delete and cease use of the Materials. Sections 6 and 8 shall survive the termination of this Agreement.
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+ 8. Governing Law and Jurisdiction.
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+ a. This Agreement and any dispute arising out of or relating to it will be governed by the laws of China, without regard to conflict of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement.
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+ b. The People's Courts in Hangzhou City shall have exclusive jurisdiction over any dispute arising out of this Agreement.
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+ 9. Other Terms and Conditions.
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+ a. Any arrangements, understandings, or agreements regarding the Material not stated herein are separate from and independent of the terms and conditions of this Agreement. You shall request a separate license from us, if you use the Materials in ways not expressly agreed to in this Agreement.
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+ b. We shall not be bound by any additional or different terms or conditions communicated by you unless expressly agreed.
README.md ADDED
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+ ---
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+ license: other
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+ license_name: qwen-research
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+ license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct/blob/main/LICENSE
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+ language:
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+ - en
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+ base_model:
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+ - Qwen/Qwen2.5-Coder-3B-Instruct
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+ pipeline_tag: text-generation
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+ library_name: transformers
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+ tags:
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+ - code
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+ - codeqwen
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+ - chat
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+ - qwen
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+ - qwen-coder
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+ ---
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+
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+
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+ # Qwen2.5-Coder-3B-Instruct-GPTQ-Int4
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+
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+ ## Introduction
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+
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+ Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. All of these models follows the Apache License (except for the 3B); Qwen2.5-Coder brings the following improvements upon CodeQwen1.5:
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+
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+ - 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. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o.
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+ - 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.
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+ - **Long-context Support** up to 128K tokens.
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+
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+ **This repo contains the GPTQ-quantized 4-bit instruction-tuned 3B Qwen2.5-Coder model**, which has the following features:
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+ - Type: Causal Language Models
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+ - Training Stage: Pretraining & Post-training
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+ - Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings
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+ - Number of Parameters: 3.09B
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+ - Number of Paramaters (Non-Embedding): 2.77B
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+ - Number of Layers: 36
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+ - Number of Attention Heads (GQA): 16 for Q and 2 for KV
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+ - Context Length: Full 131,072 tokens
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+ - Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts.
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+ - Quantization: GPTQ 4-bit
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+
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+ For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/), [GitHub](https://github.com/QwenLM/Qwen2.5-Coder), [Documentation](https://qwen.readthedocs.io/en/latest/), [Arxiv](https://arxiv.org/abs/2409.12186).
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+
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+ ## Requirements
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+
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+ 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`.
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+
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+ With `transformers<4.37.0`, you will encounter the following error:
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+ ```
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+ KeyError: 'qwen2'
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+ ```
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+
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+ Also check out our [GPTQ documentation](https://qwen.readthedocs.io/en/latest/quantization/gptq.html) for more usage guide.
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+
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+ ## Quickstart
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+
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+ Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
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+
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ model_name = "Qwen/Qwen2.5-Coder-3B-Instruct-GPTQ-Int4"
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+
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+ model = AutoModelForCausalLM.from_pretrained(
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+ model_name,
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+ torch_dtype="auto",
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+ device_map="auto"
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+ )
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+ tokenizer = AutoTokenizer.from_pretrained(model_name)
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+
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+ prompt = "write a quick sort algorithm."
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+ messages = [
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+ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
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+ {"role": "user", "content": prompt}
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+ ]
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+ text = tokenizer.apply_chat_template(
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+ messages,
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+ tokenize=False,
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+ add_generation_prompt=True
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+ )
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+ model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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+
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+ generated_ids = model.generate(
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+ **model_inputs,
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+ max_new_tokens=512
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+ )
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+ generated_ids = [
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+ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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+ ]
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+
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+ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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+ ```
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+
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+ ### Processing Long Texts
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+
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+ The current `config.json` is set for context length up to 32,768 tokens.
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+ To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
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+
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+ For supported frameworks, you could add the following to `config.json` to enable YaRN:
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+ ```json
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+ {
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+ ...,
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+ "rope_scaling": {
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+ "factor": 4.0,
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+ "original_max_position_embeddings": 32768,
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+ "type": "yarn"
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+ }
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+ }
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+ ```
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+
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+ For deployment, we recommend using vLLM.
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+ Please refer to our [Documentation](https://qwen.readthedocs.io/en/latest/deployment/vllm.html) for usage if you are not familar with vLLM.
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+ Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**.
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+ We advise adding the `rope_scaling` configuration only when processing long contexts is required.
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+
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+ ## Evaluation & Performance
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+
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+ Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/).
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+
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+ For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
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+
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+ ## Citation
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+
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+ If you find our work helpful, feel free to give us a cite.
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+
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+ ```
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+ @article{hui2024qwen2,
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+ title={Qwen2. 5-Coder Technical Report},
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+ 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},
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+ journal={arXiv preprint arXiv:2409.12186},
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+ year={2024}
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+ }
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+ @article{qwen2,
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+ title={Qwen2 Technical Report},
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+ 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},
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+ journal={arXiv preprint arXiv:2407.10671},
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+ year={2024}
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+ }
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+ ```