初始化项目，由ModelHub XC社区提供模型

Model: kitsdk/bonito-chinese-v1
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+datasets:
+- kitsdk/ctga-v1-1w-chinese
+base_model:
+- Qwen/Qwen2.5-3B
+tasks:
+  - text-generation
+  - question-answering
+  - text-ranking
+frameworks: PyTorch
+language:
+  - zh
+base_model_relation: finetune
+---
+# Bonito（支持中文版本）
+
+Bonito is an open-source model for conditional task generation: the task of converting unannotated text into task-specific training datasets for instruction tuning. This repo is a lightweight library for Bonito to easily create synthetic datasets built on top of the Hugging Face `transformers` and `vllm` libraries.
+
+- Paper: [Learning to Generate Instruction Tuning Datasets for
+Zero-Shot Task Adaptation](https://arxiv.org/abs/2402.18334)
+- Model: [bonito-v1](https://huggingface.co/BatsResearch/bonito-v1)(English)
+- Model: [bonito-chinese-v1](https://huggingface.co/kitsdk/bonito-chinese-v1)(中文)
+- Demo: [Bonito on Spaces](https://huggingface.co/spaces/nihalnayak/bonito)(English)
+- Demo: [Google Colab](https://colab.research.google.com/drive/1vZ78RFywM1pkuGpxGWGLude_L2XZ5wpw?usp=sharing)(中文)
+- Dataset: [ctga-v1](https://huggingface.co/datasets/BatsResearch/ctga-v1)(English)
+- Code: To reproduce experiments in our paper, see [nayak-aclfindings24-code](https://github.com/BatsResearch/nayak-aclfindings24-code).
+
+![Bonito](https://nihalnayak.github.io/assets/img/workflow.png)
+
+
+## This version supports the Chinese language
+Because of the training data limitations, this version supports only the 3 task types
+- 🐠 1.question generation.
+- 🐡 2.multiple-choice question answering.
+- 🐟 3.question answering without choices.
+## Google Colab: [Demo](https://colab.research.google.com/drive/1vZ78RFywM1pkuGpxGWGLude_L2XZ5wpw?usp=sharing)
+## Basic Usage
+To generate synthetic instruction tuning dataset using Bonito, you can use the following code:   
+pip3 install bonito-llm
+```python
+from pprint import pprint
+from datasets import Dataset
+from vllm import SamplingParams
+from transformers import set_seed
+from bonito import Bonito
+
+unannotated_paragraph = """灌区以往的闸门控制系统在实际应用过程中普遍以人工操作为主，容易受到多种因素的影响，不可避免出现较多缺陷。如操作人员自身的综合能力、业务水平、工作态度等对工作质量和效率产生较大影响；工作人员实践操作中遇到极端气候、工作环境恶劣等问题，大大增加了工作难度，并存在较多安全隐患。"""
+pprint(unannotated_paragraph)
+bonito = Bonito("kitsdk/bonito-chinese-v1")
+set_seed(2)
+
+def convert_to_dataset(text):
+  dataset = Dataset.from_list([{"input": text}])
+  return dataset
+
+sampling_params = SamplingParams(max_tokens=256, top_p=0.95, temperature=0.5, n=1)
+synthetic_dataset = bonito.generate_tasks(
+    convert_to_dataset(unannotated_paragraph),
+    context_col="input",
+    task_type="mcqa",
+    sampling_params=sampling_params
+)
+pprint("----Generated Instructions----")
+pprint(f'Input: {synthetic_dataset[0]["input"]}')
+pprint(f'Output: {synthetic_dataset[0]["output"]}')
+```

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+  ],
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+  "max_position_embeddings": 32768,
+  "max_window_layers": 36,
+  "model_type": "qwen2",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 1000000.0,
+  "sliding_window": 32768,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.37.2",
+  "use_cache": true,
+  "use_mrope": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

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+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}