Update README.md

README.md

@@ -1,3 +1,59 @@
 ---
 license: bigscience-bloom-rail-1.0
+language:
+- zh
 ---
+
+## 介绍
+1. ✅ 对`bloom-7b`模型做了sft
+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)
+
+## 个人感受
+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！
+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）
+3. 😛 当前的这个`bloom-7b`模型，我是非常喜欢滴，特地在`8xA100`机器上训练了部分数据。整体效果非常不错~
+
+## 如何使用
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+checkpoint = "yuanzhoulvpi/chinese_bloom_7b_chat"#"bigscience/bloomz-3b" #"bigscience/bloom-7b1"#  "output_dir/checkpoint-8260"#
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = AutoModelForCausalLM.from_pretrained(checkpoint).half().cuda()
+
+PROMPT_DICT = {
+    "prompt_input": (
+        "Below is an instruction that describes a task, paired with an input that provides further context. "
+        "Write a response that appropriately completes the request.\n\n"
+        "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:"
+    ),
+    "prompt_no_input": (
+        "Below is an instruction that describes a task. "
+        "Write a response that appropriately completes the request.\n\n"
+        "### Instruction:\n{instruction}\n\n### Response:"
+    ),
+}
+
+from typing import Optional
+def generate_input(instruction:Optional[str]= None, input_str:Optional[str] = None) -> str:
+    if input_str is None:
+        return PROMPT_DICT['prompt_no_input'].format_map({'instruction':instruction})
+    else:
+        return PROMPT_DICT['prompt_input'].format_map({'instruction':instruction, 'input':input_str})
+
+
+for i in range(5):
+    print("*"*80)
+
+    inputs = tokenizer.encode(generate_input(instruction="你是谁"), return_tensors="pt")
+    outputs = model.generate(inputs,num_beams=3,
+                            max_new_tokens=512,
+                            do_sample=False, 
+                            top_k=10,
+                            penalty_alpha=0.6,
+                            temperature=0.8,
+                            repetition_penalty=1.2)
+    print(tokenizer.decode(outputs[0]))
+```