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--- |
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license: bigscience-bloom-rail-1.0 |
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datasets: |
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- BelleGroup/train_1M_CN |
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language: |
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- zh |
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library_name: transformers |
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--- |
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## 介绍 |
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1. ✅ 对`bloom-560m`模型做了sft，在这个数量级和模型规模下，效果非常好！ |
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2. 🚀 训练代码和推理代码全部分享，可以查看链接[https://github.com/yuanzhoulvpi2017/zero_nlp/tree/main/chinese_bloom](https://github.com/yuanzhoulvpi2017/zero_nlp/tree/main/chinese_bloom) |
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## 个人感受 |
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1. 🎯 `bloom`系列的模型，在中文领域，具有极大的潜力，在经过有监督微调训练之后，效果非常惊人！ |
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2. 🔄 `bloom`系列的模型，覆盖中文、英文、代码、法语、西班牙语等。即使拿来做翻译、拿来做代码生成，也都没问题！（后期将会分享相关教程） |
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3. 😛 当前的这个`bloom-560m`模型，也只是为了跑通整个训练流程，可以无缝切换模型到不同尺寸，如`3b`、`7b`等！ |
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## 如何使用 |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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checkpoint = "yuanzhoulvpi/chinese_bloom_560m"#"bigscience/bloomz-3b" #"bigscience/bloom-7b1"# "output_dir/checkpoint-8260"# |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
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model = AutoModelForCausalLM.from_pretrained(checkpoint) |
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PROMPT_DICT = { |
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"prompt_input": ( |
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"Below is an instruction that describes a task, paired with an input that provides further context. " |
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"Write a response that appropriately completes the request.\n\n" |
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"### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:" |
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), |
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"prompt_no_input": ( |
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"Below is an instruction that describes a task. " |
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"Write a response that appropriately completes the request.\n\n" |
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"### Instruction:\n{instruction}\n\n### Response:" |
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), |
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} |
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from typing import Optional |
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def generate_input(instruction:Optional[str]= None, input_str:Optional[str] = None) -> str: |
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if input_str is None: |
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return PROMPT_DICT['prompt_no_input'].format_map({'instruction':instruction}) |
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else: |
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return PROMPT_DICT['prompt_input'].format_map({'instruction':instruction, 'input':input_str}) |
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for i in range(5): |
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print("*"*80) |
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inputs = tokenizer.encode(generate_input(instruction="你是谁"), return_tensors="pt") |
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outputs = model.generate(inputs,num_beams=3, |
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max_new_tokens=512, |
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do_sample=False, |
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top_k=10, |
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penalty_alpha=0.6, |
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temperature=0.8, |
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repetition_penalty=1.2) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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### example1 |
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1. 训练前效果 |
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```bash |
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Below is an instruction that describes a task. Write a response that appropriately completes the request. |
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### Instruction: |
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写一篇关于水杯的文章 |
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### Response: 杯子是用来喝水用的</s> |
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``` |
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2. 训练后效果 |
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```bash |
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Below is an instruction that describes a task. Write a response that appropriately completes the request. |
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### Instruction: |
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写一篇关于水杯的文章 |
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### Response: |
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水杯是人们日常生活中必不可少的物品之一，它能够帮助人们盛放、储存和运输水。以下是一些有关水杯的详细信息： |
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1. 形状：水杯通常有圆形、方形、长方形等不同形状。 |
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2. 容量：水杯通常有10毫升、20毫升、30毫升、40毫升等不同容量。 |
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3. 材质：水杯通常有玻璃、陶瓷、金属等不同材质。 |
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4. 颜色：水杯通常有红色、蓝色、黄色、绿色等不同颜色。 |
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5. 用途：水杯通常用于盛放、储存和运输水，如饮料、果汁、茶、咖啡等。 |
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6. 功能：水杯可以用来盛放、储存和运输各种液体，如饮料、果汁、茶、咖啡等。 |
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7. 用途广泛：水杯不仅用于盛放、储存和运输水，还可以用于制作各种饮料、果汁、茶、咖啡等。 |
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总之，水杯是一个不可或缺的物品，它能够帮助人们盛放、储存和运输水，同时还可以用于制作各种饮料、果汁、茶、咖啡等。</s> |
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``` |
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### example 2 |
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1. 训练前效果 |
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```bash |
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Below is an instruction that describes a task. Write a response that appropriately completes the request. |
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### Instruction: |
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你是谁 |
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### Response: I am a student.</s> |
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``` |
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2. 训练后效果 |
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```bash |
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Below is an instruction that describes a task. Write a response that appropriately completes the request. |
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### Instruction: |
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你是谁 |
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### Response:我是一个AI语言模型，没有个人身份。</s> |
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``` |
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