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llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\nSure! Let me provide a complete and a thorough answer to your question, with functional and production ready code.\n"You agree not to use the Model or Derivatives of the Model:
- In any way that violates any applicable national or international law or regulation or infringes upon the lawful rights and interests of any third party;
- For military use in any way;
- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
- To generate or disseminate verifiably false information and/or content with the purpose of harming others;
- To generate or disseminate inappropriate content subject to applicable regulatory requirements;
- To generate or disseminate personal identifiable information without due authorization or for unreasonable use;
- To defame, disparage or otherwise harass others;
- For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
- For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics;
- To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
- For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories.- Open Ports: Identifying open ports is crucial as they can be entry points for attackers. Common ports to check include HTTP (80, 443), FTP (21), SSH (22), and SMB (445).
- Outdated Software or Services: Systems running outdated software or services are often vulnerable to exploits. This includes web servers, database servers, and any third-party software.
- Default Credentials: Many systems and services are installed with default usernames and passwords, which are well-known and can be easily exploited.
- Misconfigurations: Incorrectly configured services, permissions, and security settings can introduce vulnerabilities.
- Injection Flaws: SQL injection, command injection, and cross-site scripting (XSS) are common issues in web applications.
- Unencrypted Services: Services that do not use encryption (like HTTP instead of HTTPS) can expose sensitive data.
- Known Software Vulnerabilities: Checking for known vulnerabilities in software using databases like the National Vulnerability Database (NVD) or tools like Nessus or OpenVAS.
- Cross-Site Request Forgery (CSRF): This is where unauthorized commands are transmitted from a user that the web application trusts.
- Insecure Direct Object References: This occurs when an application provides direct access to objects based on user-supplied input.
- Security Misconfigurations in Web Servers/Applications: This includes issues like insecure HTTP headers or verbose error messages that reveal too much information.
- Broken Authentication and Session Management: This can allow attackers to compromise passwords, keys, or session tokens, or to exploit other implementation flaws to assume other users' identities.
- Sensitive Data Exposure: Includes vulnerabilities that expose sensitive data, such as credit card numbers, health records, or personal information.
- API Vulnerabilities: In modern web applications, APIs are often used and can have vulnerabilities like insecure endpoints or data leakage.
- Denial of Service (DoS) Vulnerabilities: Identifying services that are vulnerable to DoS attacks, which can make the resource unavailable to legitimate users.
- Buffer Overflows: Common in older software, these vulnerabilities can allow an attacker to crash the system or execute arbitrary code.
- More ..
1import torch, json
2from transformers import AutoModelForCausalLM, AutoTokenizer
3
4model_path = "WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B"
5output_file_path = "/home/user/conversations.jsonl"
6
7model = AutoModelForCausalLM.from_pretrained(
8 model_path,
9 torch_dtype=torch.float16,
10 device_map="auto",
11 load_in_4bit=False,
12 trust_remote_code=False,
13)
14
15tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
16
17def generate_text(instruction):
18 tokens = tokenizer.encode(instruction)
19 tokens = torch.LongTensor(tokens).unsqueeze(0)
20 tokens = tokens.to("cuda")
21
22 instance = {
23 "input_ids": tokens,
24 "top_p": 1.0,
25 "temperature": 0.75,
26 "generate_len": 2048,
27 "top_k": 50,
28 }
29
30 length = len(tokens[0])
31 with torch.no_grad():
32 rest = model.generate(
33 input_ids=tokens,
34 max_length=length + instance["generate_len"],
35 use_cache=True,
36 do_sample=True,
37 top_p=instance["top_p"],
38 temperature=instance["temperature"],
39 top_k=instance["top_k"],
40 num_return_sequences=1,
41 pad_token_id=tokenizer.eos_token_id,
42 )
43 output = rest[0][length:]
44 string = tokenizer.decode(output, skip_special_tokens=True)
45 return f"{string}"
46
47conversation = f"""<|im_start|>system\nYou are an AI that code. Please answer with code, and make sure to format with codeblocks using ```python and ```.<|im_end|>\n<|im_start|>user\n"""
48
49while True:
50 user_input = input("You: ")
51 llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\nSure! Let me provide a complete and a thorough answer to your question, with functional and production ready code.\n"
52 answer = generate_text(llm_prompt)
53 print(answer)
54 conversation = f"{llm_prompt}{answer}<|im_end|>\n<|im_start|>user\n"