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--- |
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base_model: AicoresSecurity/Cybernet-Sec-3B-R1-V0 |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- llama |
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- trl |
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license: llama3.2 |
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language: |
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- en |
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--- |
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## Model Description |
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AI Cores LLM that is fine tune with cybersecurity dataset for cybersecurity applications. It provides safety advise for python code, especially for Gen AI developers. |
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## Intended Use |
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- **Intended users**: Application Engineers, Software Engineers, Data scientists and developers working on cybersecurity applications. |
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- **Out-of-scope use cases**: This model should not be used for medical advice, legal decisions, or any life-critical systems. |
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# How to use |
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Use with transformers |
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Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function. |
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Make sure to update your transformers and bitsandbytes installation via pip install --upgrade transformers. pip install --upgrade bitsandbytes accelerate torch |
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Inference: |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig |
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# Create a BitsAndBytesConfig for 8-bit quantization |
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bnb_config = BitsAndBytesConfig( |
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load_in_8bit=True |
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) |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("AicoresSecurity/Cybernet-Sec-3B-R1-V0.1") |
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if torch.cuda.is_available(): |
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from transformers import BitsAndBytesConfig |
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bnb_config = BitsAndBytesConfig(load_in_8bit=True) |
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model = AutoModelForCausalLM.from_pretrained( |
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"AicoresSecurity/Cybernet-Sec-3B-R1-V0.1", |
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quantization_config=bnb_config, |
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device_map="auto" |
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) |
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else: |
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# Fallback for CPU-only systems |
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model = AutoModelForCausalLM.from_pretrained("AicoresSecurity/Cybernet-Sec-3B-R1-V0.1") |
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# Define your system prompt and user prompt |
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system_prompt = """You will be provided with a piece of Python code, and your task is to provide ideas for efficiency improvements ans safety. Only code |
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<reasoning> |
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... |
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</reasoning>""" |
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user_prompt = """ |
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import os |
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def process_filename(filename): |
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# Sanitize the filename to prevent path traversal attacks |
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allowed_chars = "abcdefghijklmnopqrstuvwxyz0123456789_-" |
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sanitized_filename = ''.join(c for c in filename if c in allowed_chars) |
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if sanitized_filename != filename: |
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raise ValueError("Invalid filename: only alphanumeric characters, underscores, and hyphens are allowed.") |
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filepath = os.path.join("/user_data", sanitized_filename) |
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try: |
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with open(filepath, 'r') as file: |
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content = file.read() |
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return content |
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except FileNotFoundError: |
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return "File not found." |
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# Example usage |
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filename = "user_report.txt" |
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print(process_filename(filename)) |
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filename_attack = "../../../etc/passwd" |
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try: |
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print(process_filename(filename_attack)) |
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except ValueError as e: |
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print(e) |
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""" |
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full_prompt = system_prompt + user_prompt |
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# Tokenize the full prompt and move it to the model's device |
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input_ids = tokenizer.encode(full_prompt, return_tensors="pt").to(model.device) |
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# Generate output from the model |
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output_ids = model.generate( |
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input_ids, |
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max_length=100, # Adjust as needed |
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do_sample=True, # Use sampling for more varied output |
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temperature=0.7, # Adjust for creativity |
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) |
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# Decode the generated tokens back into a string |
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output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True) |
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print(output_text) |
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``` |
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# Uploaded model |
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- **Developed by:** AicoresSecurity |
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- **License:** apache-2.0 |
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- **Finetuned from model :** AicoresSecurity/Cybernet-Sec-3B-R1-V0 |
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. |
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
