Create README.md

README.md

@@ -1,3 +1,59 @@
 ---
 license: apache-2.0
+datasets:
+- w8ay/security-paper-datasets
 ---
+
+## 使用
+商业模型对于网络安全领域问题大多会有道德限制，所以基于网络安全数据训练了一个模型，模型基于Baichuan 13B，模型参数大小130亿，至少需要30G显存运行，35G最佳。
+- transformers
+- peft
+
+**模型加载**
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+from peft import PeftModel
+device = 'auto'
+tokenizer = AutoTokenizer.from_pretrained("w8ay/secgpt", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("w8ay/secgpt", 
+                                             trust_remote_code=True,
+                                             device_map=device,
+                                             torch_dtype=torch.float16)
+print("模型加载成功")
+
+```
+**调用**
+```python
+def reformat_sft(instruction, input):
+    if input:
+        prefix = (
+            "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"
+            f"### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:"
+        )
+    else:
+        prefix = (
+            "Below is an instruction that describes a task. "
+            "Write a response that appropriately completes the request.\n"
+            f"### Instruction:\n{instruction}\n\n### Response:"
+        )
+    return prefix
+
+query = '''介绍sqlmap如何使用'''
+query = reformat_sft(query,'')
+
+generation_kwargs = {
+    "top_p": 0.7,
+    "temperature": 0.3,
+    "max_new_tokens": 2000,
+    "do_sample": True,
+    "repetition_penalty":1.1
+}
+
+inputs = tokenizer.encode(query, return_tensors='pt', truncation=True)
+inputs = inputs.cuda()
+generate = model.generate(input_ids=inputs, **generation_kwargs)
+output = tokenizer.decode(generate[0])
+print(output)
+```