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LICENSE

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+Tongyi Qianwen LICENSE AGREEMENT
+
+Tongyi Qianwen Release Date: August 3, 2023
+
+By clicking to agree or by using or distributing any portion or element of the Tongyi Qianwen Materials, you will be deemed to have recognized and accepted the content of this Agreement, which is effective immediately.
+
+1. Definitions
+    a. This Tongyi Qianwen LICENSE AGREEMENT (this "Agreement") shall mean the terms and conditions for use, reproduction, distribution and modification of the Materials as defined by this Agreement.
+    b. "We"(or "Us") shall mean Alibaba Cloud.
+    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.
+    d. "Third Parties" shall mean individuals or legal entities that are not under common control with Us or You.
+    e. "Tongyi Qianwen" shall mean the large language models (including Qwen model and Qwen-Chat model), 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.
+    f. "Materials" shall mean, collectively, Alibaba Cloud's proprietary Tongyi Qianwen and Documentation (and any portion thereof) made available under this Agreement.
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README.md

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+---
+license: other
+license_name: tongyi-qianwen
+license_link: >-
+  https://huggingface.co/Qwen/CodeQwen1.5-7B-Chat/blob/main/LICENSE
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- chat
+- gptq
+- int8
+studios:
+- qwen/CodeQwen1.5-7b-Chat-demo
+---
+
+# CodeQwen1.5-7B-Chat
+
+## About Quantization
+我们使用modelscope [swift](https://github.com/modelscope/swift/)仓库进行GPTQ量化. 量化文档可以查看[这里](https://github.com/modelscope/swift/blob/main/docs/source/LLM/LLM%E9%87%8F%E5%8C%96%E6%96%87%E6%A1%A3.md). 量化命令如下:
+
+We use the modelscope [swift](https://github.com/modelscope/swift/) repository to perform GPTQ quantization. Quantization documentation can be found [here](https://github.com/modelscope/swift/blob/main/docs/source_en/LLM/LLM-quantization.md). The quantization command is as follows:
+
+```bash
+OMP_NUM_THREADS=14 CUDA_VISIBLE_DEVICES=0 swift export \
+    --model_type codeqwen1half-7b-chat --quant_bits 8 \
+    --dataset codefuse-evol-instruction-zh --quant_method gptq --quant_seqlen 8192
+```
+
+## Introduction
+
+CodeQwen1.5 is the Code-Specific version of Qwen1.5. It is a transformer-based decoder-only language model pretrained on a large amount of data of codes. 
+
+* Strong code generation capabilities and competitve performance across a series of benchmarks;
+* Supporting long context understanding and generation with the context length of 64K tokens;
+* Supporting 92 coding languages
+* Excellent performance on text-to-SQL, bug fix, etc.
+
+
+For more details, please refer to our [blog post](https://qwenlm.github.io/blog/codeqwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5).
+
+## Model Details
+CodeQwen1.5 is based on Qwen1.5, a language model series including decoder language models of different model sizes. It is trained on 3 trillion tokens of data of codes, and it includes group query attention (GQA) for efficient inference.
+
+
+## Requirements
+The code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might 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 modelscope import AutoModelForCausalLM, AutoTokenizer
+device = "cuda" # the device to load the model onto
+
+model = AutoModelForCausalLM.from_pretrained(
+    "huangjintao/CodeQwen1.5-7B-Chat-GPTQ-Int8",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("huangjintao/CodeQwen1.5-7B-Chat-GPTQ-Int8")
+
+prompt = "Write a quicksort algorithm in python."
+messages = [
+    {"role": "system", "content": "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(device)
+
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    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]
+print(response)
+```
+
+
+
+## Tips
+
+* If you encounter code switching or other bad cases, we advise you to use our provided hyper-parameters in `generation_config.json`.
+
+
+## Citation
+
+If you find our work helpful, feel free to give us a cite.
+
+```
+@article{qwen,
+  title={Qwen Technical Report},
+  author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu},
+  journal={arXiv preprint arXiv:2309.16609},
+  year={2023}
+}
+```

config.json

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+{
+  "_name_or_path": "/mnt/nas2/huangjintao.hjt/.cache/modelscope/hub/qwen/CodeQwen1___5-7B-Chat",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 2,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 13440,
+  "max_position_embeddings": 65536,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 4,
+  "quantization_config": {
+    "bits": 8,
+    "damp_percent": 0.1,
+    "desc_act": false,
+    "group_size": 128,
+    "modules_in_block_to_quantize": null,
+    "quant_method": "gptq",
+    "sym": true,
+    "true_sequential": true
+  },
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 1000000,
+  "rotary_emb_base": 1000000,
+  "seq_length": 65536,
+  "sliding_window": 65536,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.39.3",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 92416
+}

configuration.json

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+{
+    "framework": "pytorch",
+    "task": "text-generation",
+    "allow_remote": true
+}

generation_config.json

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+{
+  "bos_token_id": 2,
+  "do_sample": true,
+  "eos_token_id": [
+    4,
+    2
+  ],
+  "pad_token_id": 92298,
+  "repetition_penalty": 1.0,
+  "temperature": 1.0,
+  "top_p": 0.95,
+  "transformers_version": "4.39.3"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b1561edbccd9fdebb3385df5ab000df9d2d135b7186027a0cc84fa9c29e03207
+size 8167204608

quantize_config.json

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+{
+  "bits": 8,
+  "dataset": "codefuse-evol-instruction-zh",
+  "group_size": 128,
+  "damp_percent": 0.1,
+  "desc_act": false,
+  "sym": true,
+  "true_sequential": true,
+  "quant_method": "gptq",
+  "modules_in_block_to_quantize": null
+}

special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<fim_prefix>",
+    "<fim_middle>",
+    "<fim_suffix>",
+    "<fim_pad>"
+  ],
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": "<|im_end|>",
+  "pad_token": {
+    "content": "<fim_pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}