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Narada-3.2-3B-v1

Text Classification
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English
llama
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text-generation-inference
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chore: add Narada-3.2-3B-v1 model artifacts

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  1. .gitattributes +35 -0
  2. .gitignore +5 -0
  3. README.md +102 -0
  4. chat_template.jinja +93 -0
  5. config.json +39 -0
  6. generation_config.json +14 -0
  7. model-00001-of-00002.safetensors +3 -0
  8. model-00002-of-00002.safetensors +3 -0
  9. model.safetensors.index.json +262 -0
  10. special_tokens_map.json +16 -0
  11. system_prompt.md +104 -0
  12. tokenizer.json +0 -0
  13. tokenizer_config.json +2062 -0
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README.md ADDED
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+ ---
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+ language:
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+ - en
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+ base_model:
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+ - meta-llama/Llama-3.2-3B-Instruct
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+ pipeline_tag: text-generation
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+ tags:
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+ - code
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+ - text-generation-inference
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+ ---
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+ # Model Card
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+
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+ `Narada` is a Supervised Fine-Tuned (SFT) variant of the Llama 3.2 3B Instruct model, specialized for classifying detected secrets in code snippets. It takes as input a code snippet with prepended line numbers for each line, along with the specific line number indicating the location of the potential secret, and outputs a classification label of "true positive" (actual sensitive secret) or "false positive" (benign or non-sensitive string).
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+
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+
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+ ## Model Details
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+
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+ ### Model Description
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+ This is a specialized(fine-tuned) variant of Meta's Llama 3.2 3B Instruct model, refined through Supervised Fine-Tuning (SFT) for binary classification of potential secrets/credentials in code snippets. As a lightweight transformer-based LLM with ~3 billion parameters, it's optimized for efficient inference on standard hardware. The SFT process adapts the base model to distinguish genuine sensitive information (e.g., API keys, passwords, private keys) from benign strings, addressing the common issue of false positives in automated secret scanners.
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+
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+ #### Core Functionality
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+ The model evaluates "secrets" flagged in code, classifying them as "True Positive" (TP: actual risk if exposed) or "False Positive" (FP: non-sensitive, like placeholders or test values). It uses a structured evaluation prompt (detailed in the attached system prompt) that emphasizes context analysis, secret categorization (e.g., authentication credentials, cryptographic material, third-party tokens), false positive detection (e.g., low-entropy strings, dev artifacts), and entropy/format checks. Outputs include the classification label, exact secret value (preserving original structure for multiline cases), and a concise reasoning explanation.
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+
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+ #### Input and Output
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+ **Input:** A code snippet with prepended line numbers (e.g., "1: import os\n2: API_KEY = 'sk-12345'"), plus the target line number for the suspected secret. Formatted as an instruct prompt:
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+ ```py
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+ user_prompt = f"<input>\n# CODE:\n{code}\n\n# LINE NUMBER: {line_number}\n</input>"
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+ ```
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+ Where `code` is the code snippet you want to analyze, and `line_number` is the location of the secret in the snippet.
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+
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+ **Output:** Valid JSON only, example:
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+ ```json
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+ {"line_number": 2, "label": "True Positive", "secret_value": "sk-12345", "reason": "High-entropy string matching OpenAI API key format, assigned to sensitive variable in production-like context."}
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+ ```
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+ This ensures easy integration into tools.
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+
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+ ## How to Get Started with the Model
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+
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+ Use the code below to get started with the model:
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+ ```py
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+ import torch
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+
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+ # Load the model and tokenizer
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+ model_name = "deepsource/Narada-3.2-3B-v1"
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+ device = "cuda" if torch.cuda.is_available() else "cpu"
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+ model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16).to(device)
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+ tokenizer = AutoTokenizer.from_pretrained(model_name)
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+
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+ # Prepare input: Format the code snippet (with prepended line numbers) and target line number as shown
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+ code = """1: import requests
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+ 2: import sys
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+ 3:
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+ 4: class GitHubClient:
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+ 5: def __init__(self):
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+ 6: # Hardcoded API token (bad practice, for demo only)
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+ 7: self.token = "ghp_9aB3XyZkLmN8QrT5UvWj7xY2ZaQ4b9CdEfGh"
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+ 8: self.base_url = "https://api.github.com"
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+ 9:
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+ 10: def get_user(self, username):
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+ 11: headers = {
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+ 12: "Authorization": f"token {self.token}",
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+ 13: "Accept": "application/vnd.github.v3+json"
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+ 14: }
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+ 15: url = f"{self.base_url}/users/{username}"
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+ 16: response = requests.get(url, headers=headers)
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+ 17: if response.status_code == 200:
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+ 18: return response.json()
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+ 19: else:
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+ 20: return {"error": response.status_code, "message": response.text}
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+ """
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+ line_number = "7"
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+ input_text = f"<input>\n# CODE:\n```{code}```\n\n# LINE NUMBER: {line_number}\n</input>"
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+
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+ system_prompt = "<replace_with_system_prompt>" # Replace with the system prompt present at https://huggingface.co/deepsource/Narada-3.2-3B-v1/blob/main/system_prompt.md
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+ messages = [
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+ {"role": "system", "content": system_prompt}
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+ {"role": "user", "content": input_text}
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+ ]
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+ input_ids = tokenizer.apply_chat_template(messages, tokenize=True, return_tensors="pt").to(device)
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+
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+ # Generate response
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+ outputs = model.generate(input_ids, max_new_tokens=1024)
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+ generated_tokens = outputs[0][input_ids.shape[1]:]
85
+ response = tokenizer.decode(generated_tokens, skip_special_tokens=True).strip()
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+
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+ # Parse the JSON output (model responds with <json>{...}</json>)
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+ import re
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+ import json
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+ match = re.search(r'<json>\s*(.*?)\s*</json>', response, re.DOTALL)
91
+ if match:
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+ json_str = match.group(1)
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+ data = json.loads(json_str)
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+ print(data) # Example output: {'line_number': 7, 'label': 'True Positive', 'secret_value': 'ghp_9aB3XyZkLmN8QrT5UvWj7xY2ZaQ4b9CdEfGh', 'reason': '...'}
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+ else:
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+ raise ValueError("No valid JSON found in response")
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+ ```
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+
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+ ## Bias, Risks, and Limitations
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+
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+ - The model is in the early stages of development, so errors and misclassifications are expected, particularly with obfuscated secrets (e.g., encoded, split across lines, or dynamically generated), emerging or custom secret types not well-represented in the training data, or code in niche languages.
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+ - It relies on structured inputs (code snippets with line numbers and a specified line), and deviations may lead to unreliable outputs.
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+ {{- bos_token }}
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+ {%- if custom_tools is defined %}
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+ {%- set tools = custom_tools %}
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+ {%- endif %}
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+ {%- if not tools_in_user_message is defined %}
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+ {%- set tools_in_user_message = true %}
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+ {%- endif %}
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+ {%- if not date_string is defined %}
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+ {%- if strftime_now is defined %}
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+ {%- set date_string = strftime_now("%d %b %Y") %}
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+ {%- else %}
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+ {%- set date_string = "26 Jul 2024" %}
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+ {%- endif %}
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+ {%- endif %}
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+ {%- if not tools is defined %}
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+ {%- set tools = none %}
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+ {%- endif %}
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+
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+ {#- This block extracts the system message, so we can slot it into the right place. #}
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+ {%- if messages[0]['role'] == 'system' %}
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+ {%- set system_message = messages[0]['content']|trim %}
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+ {%- set messages = messages[1:] %}
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+ {%- else %}
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+ {%- set system_message = "" %}
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+ {%- endif %}
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+
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+ {#- System message #}
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+ {{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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+ {%- if tools is not none %}
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+ {{- "Environment: ipython\n" }}
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+ {%- endif %}
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+ {{- "Cutting Knowledge Date: December 2023\n" }}
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+ {{- "Today Date: " + date_string + "\n\n" }}
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+ {%- if tools is not none and not tools_in_user_message %}
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+ {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
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+ {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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+ {{- "Do not use variables.\n\n" }}
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+ {%- for t in tools %}
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+ {{- t | tojson(indent=4) }}
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+ {{- "\n\n" }}
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+ {%- endfor %}
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+ {%- endif %}
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+ {{- system_message }}
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+ {{- "<|eot_id|>" }}
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+
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+ {#- Custom tools are passed in a user message with some extra guidance #}
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+ {%- if tools_in_user_message and not tools is none %}
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+ {#- Extract the first user message so we can plug it in here #}
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+ {%- if messages | length != 0 %}
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+ {%- set first_user_message = messages[0]['content']|trim %}
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+ {%- set messages = messages[1:] %}
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+ {%- else %}
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+ {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
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+ {%- endif %}
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+ {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
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+ {{- "Given the following functions, please respond with a JSON for a function call " }}
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+ {{- "with its proper arguments that best answers the given prompt.\n\n" }}
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+ {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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+ {{- "Do not use variables.\n\n" }}
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+ {%- for t in tools %}
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+ {{- t | tojson(indent=4) }}
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+ {{- "\n\n" }}
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+ {%- endfor %}
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+ {{- first_user_message + "<|eot_id|>"}}
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+ {%- endif %}
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+
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+ {%- for message in messages %}
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+ {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
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+ {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
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+ {%- elif 'tool_calls' in message %}
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+ {%- if not message.tool_calls|length == 1 %}
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+ {{- raise_exception("This model only supports single tool-calls at once!") }}
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+ {%- endif %}
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+ {%- set tool_call = message.tool_calls[0].function %}
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+ {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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+ {{- '{"name": "' + tool_call.name + '", ' }}
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+ {{- '"parameters": ' }}
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+ {{- tool_call.arguments | tojson }}
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+ {{- "}" }}
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+ {{- "<|eot_id|>" }}
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+ {%- elif message.role == "tool" or message.role == "ipython" %}
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+ {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
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+ {%- if message.content is mapping or message.content is iterable %}
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+ {{- message.content | tojson }}
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+ {%- else %}
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+ {{- message.content }}
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+ {%- endif %}
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+ {{- "<|eot_id|>" }}
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+ {%- endif %}
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+ {%- endfor %}
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+ {%- if add_generation_prompt %}
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+ {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
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+ {%- endif %}
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+ "transformers_version": "4.55.0",
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+ "use_cache": true,
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+ "vocab_size": 128256
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+ }
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+ "model.norm.weight": "model-00002-of-00002.safetensors"
261
+ }
262
+ }
special_tokens_map.json ADDED
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+ "bos_token": {
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+ "eos_token": {
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+ "content": "<|eot_id|>",
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+ "rstrip": false,
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+ }
system_prompt.md ADDED
@@ -0,0 +1,104 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ You are a strict evaluator of hardcoded/exposed secrets in software code with expertise in cybersecurity and secure coding practices.
2
+ ## INPUT FORMAT
3
+ You'll receive:
4
+ - Code snippet with line numbers
5
+ - Specific line number to evaluate
6
+ ## EVALUATION PROCESS
7
+ ### Step 1: Context Analysis
8
+ - Examine the reported line and with the surrounded context provided.
9
+ - Consider file type, naming patterns, and code structure
10
+ - Identify the programming language and common patterns
11
+ ### Step 2: Secret Classification (Enhanced)
12
+ When evaluating the reported line, determine if it contains a hardcoded secret by checking for **direct or indirect indicators** of sensitive values. A candidate secret typically falls into one of these categories:
13
+ 1. **Authentication Credentials**
14
+ - API keys, OAuth tokens, JWTs, session tokens, bearer tokens
15
+ - Service account keys, private access tokens (PATs)
16
+ - Usernames paired with passwords
17
+ 2. **Database & Storage Credentials**
18
+ - Database connection strings with embedded user/password (Postgres, MySQL, MongoDB, SQL Server, etc.)
19
+ - Redis or Memcached URLs containing credentials
20
+ - Cloud storage access keys (AWS, GCP, Azure, DigitalOcean, etc.)
21
+ 3. **Cryptographic Material**
22
+ - Private keys (RSA, DSA, ECDSA, Ed25519, PGP)
23
+ - Certificates with embedded private data
24
+ - Symmetric keys (AES, DES, HMAC secrets, signing keys)
25
+ - Initialization vectors (IVs) or salts if hardcoded
26
+ 4. **Configuration Secrets**
27
+ - SMTP/FTP credentials
28
+ - VPN, proxy, or SSH credentials
29
+ - Cloud provider secret variables
30
+ 5. **Third-Party Service Tokens**
31
+ - Payment gateways (Stripe, PayPal, Razorpay, Square)
32
+ - Messaging APIs (Twilio, Slack, Telegram, Discord, WhatsApp, SendGrid)
33
+ - Analytics or monitoring services (Sentry, Datadog, New Relic)
34
+ 6. **Special Cases**
35
+ - License keys and activation codes
36
+ - Hardcoded recovery or master keys
37
+ - Any token or string matching **known provider formats** or entropy thresholds
38
+ ### Note
39
+ - If the **reported line number is the starting point of a secret**, analyze the **subsequent lines** to determine whether the secret spans multiple lines.
40
+ Examples:
41
+ - RSA/SSH private keys (-----BEGIN ...----- to -----END ...-----)
42
+ - PEM-encoded certificates
43
+ - JSON blobs containing service credentials (e.g., GCP service account key files)
44
+ - Multiline base64-encoded keys or embedded secrets
45
+ - In these cases, the **entire block** is considered the secret value, not just the single line. The extraction must include all consecutive lines until the secret is fully captured.
46
+ - If the surrounding code shows a **wrapper structure** (e.g., environment substitution, dummy placeholders, or documented examples), then it should be carefully evaluated as a **false positive candidate**, even if it superficially resembles a real secret.
47
+ ### Step 3: False Positive Detection
48
+ Mark as False Positive if ANY of these patterns match:
49
+ **Placeholders & Examples:**
50
+ - Generic placeholders and dummy values
51
+ - Tutorial or documentation examples
52
+ - Template variable syntax and substitution patterns
53
+ **Development & Testing:**
54
+ - Local development references and endpoints
55
+ - Test values and anything with test/dev/mock prefixes
56
+ - Development and testing database connections
57
+ **Low Entropy Indicators:**
58
+ - Length below minimum threshold for real secrets
59
+ - Repetitive or sequential character patterns
60
+ - Common dictionary words related to authentication
61
+ - Predictable or non-random string patterns
62
+ **Framework & Library Identifiers:**
63
+ - Service worker and build tool paths
64
+ - CDN references and public resource URLs
65
+ - Public identifiers and well-known API endpoints
66
+ - Framework-generated or library-specific identifiers
67
+ ### Step 4: Entropy & Format Analysis
68
+ For potential True Positives, verify:
69
+ - **High entropy**: Random-looking strings with mixed case, numbers, special characters, and unpredictable patterns
70
+ - **Proper format**: Matches known secret patterns and service-specific prefixes or structures
71
+ - **Sufficient length**: Meets minimum length requirements typical for the secret type
72
+ - **Context clues**: Variable names, comments, or surrounding code indicate sensitive data handling
73
+ - **Character distribution**: Balanced mix of character types without obvious patterns or repetition
74
+ - **Service alignment**: Format consistency with known API providers, cloud services, or authentication systems
75
+ - **Realistic complexity**: Complexity level appropriate for production secrets rather than test data
76
+ ### Secret Value:
77
+ You must also output the secret value that you analyzed and classified. You must output it in the secret_value field of the output JSON.
78
+ Requirements:
79
+ - Exact extraction: Return the precise secret value as it appears in the input code
80
+ - No modifications: Do not add quotes, escape characters, or formatting that wasn't in the original
81
+ - Preserve structure: Maintain original whitespace, line breaks, and indentation for multiline secrets
82
+ - Complete value: Include the full secret from start to end, regardless of length
83
+ - Context boundaries: Extract only the secret value itself, excluding variable names, operators, or surrounding code
84
+ - Special characters: Preserve all special characters, symbols, and non-printable characters as they appear
85
+ ### Reasoning:
86
+ You must provide a brief explanation of your decision that demonstrates analytical thinking for educational purposes. You must output it in the reason field of the output JSON.
87
+ Requirements:
88
+ - Step-by-step logic: Show the evaluation process from initial assessment to final classification
89
+ - Pattern recognition: Explain which specific patterns or characteristics led to your decision
90
+ - Evidence-based: Reference concrete evidence from the code (entropy level, format, context clues)
91
+ - Comparative analysis: When applicable, explain why it's not a false positive by addressing potential counterarguments
92
+ - Confidence indicators: Mention factors that increase or decrease certainty in your classification
93
+ - Educational value: Structure explanation to help other models understand the reasoning process
94
+ - Concise clarity: Keep explanation brief but comprehensive enough to be instructive
95
+ ## OUTPUT FORMAT
96
+ Respond with valid JSON only in the following format:
97
+ <json>
98
+ {
99
+ "line_number": <reported_line_number>,
100
+ "label": "True Positive" | "False Positive",
101
+ "secret_value": "<exact secret value>",
102
+ "reason": "<concise reasoning of decision>",
103
+ }
104
+ </json>
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
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