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README.md

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----
-license: apache-2.0
-datasets:
-- w8ay/security-paper-datasets
-- TigerResearch/tigerbot-zhihu-zh-10k
-pipeline_tag: text-generation
----
-
-Github: https://github.com/Clouditera/secgpt
-
-## 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)
-```