Update README.md

README.md

@@ -1,3 +1,62 @@
 ---
-license: bigscience-openrail-m
+license: bigscience-bloom-rail-1.0
+language:
+- zh
 ---
+
+
+# 体验
+ 🚀 点击链接，即可体验🔗 **[http://101.68.79.42:7861/](http://101.68.79.42:7861/)**
+
+
+## 介绍
+1. ✅ 对`bloom-7b`模型做了sft，本次版本为V2版本，相较于V1版本，效果更好！！！
+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)
+
+
+## 如何使用
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+checkpoint = "yuanzhoulvpi/chinese_bloom_7b_chat_v2"#"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]))
+```
+
+