From d474e39e773be44dfb51ab2a16d8f33381ac94d6 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Fri, 17 Jul 2026 03:30:10 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: OMCHOKSI108/Paralay1.1-Merged Source: Original Platform --- .gitattributes | 37 ++++ README.md | 294 ++++++++++++++++++++++++++ chat_template.jinja | 54 +++++ config.json | 61 ++++++ docs/pralay.gif | 3 + generation_config.json | 14 ++ model-00001-of-00002.safetensors | 3 + model-00002-of-00002.safetensors | 3 + model.safetensors.index.json | 346 +++++++++++++++++++++++++++++++ tokenizer.json | 3 + tokenizer_config.json | 30 +++ 11 files changed, 848 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 chat_template.jinja create mode 100644 config.json create mode 100644 docs/pralay.gif create mode 100644 generation_config.json create mode 100644 model-00001-of-00002.safetensors create mode 100644 model-00002-of-00002.safetensors create mode 100644 model.safetensors.index.json create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..061a6dd --- /dev/null +++ b/.gitattributes @@ -0,0 +1,37 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text +docs/pralay.gif filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..c2422a4 --- /dev/null +++ b/README.md @@ -0,0 +1,294 @@ +--- +language: +- en +license: apache-2.0 +base_model: +- Qwen/Qwen2.5-1.5B-Instruct +tags: +- cybersecurity +- security +- defensive-ai +- fine-tuned +- qwen2 +- lora +- merged +- incident-response +- threat-detection +pipeline_tag: text-generation +library_name: transformers +--- + +# Paralay 1.1 — Merged (PralayAI Cybersecurity Assistant) + +**PralayAI** is a fine-tuned, LoRA-merged large language model built on top of [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct), specialized for **defensive cybersecurity assistance**. + +Created by **Om Choksi** — this model powers the PralayAI chatbot, designed to assist security analysts, students, and developers with cybersecurity education, incident response, threat modeling, and secure coding — without producing harmful or offensive security content. + +--- + +## Live Demo + +![PralayAI Demo](docs/pralay.gif) + +--- + +## Model Details + +| Property | Value | +|---|---| +| **Base Model** | Qwen/Qwen2.5-1.5B-Instruct | +| **Fine-tuning Method** | LoRA (Low-Rank Adaptation) | +| **LoRA Adapter Repo** | [OMCHOKSI108/Paralay1.1](https://huggingface.co/OMCHOKSI108/Paralay1.1) | +| **Merged Model** | This repo — LoRA merged into base weights | +| **Parameters** | ~1.5 Billion | +| **Language** | English | +| **Domain** | Defensive Cybersecurity | +| **License** | Apache 2.0 | +| **Creator** | Om Choksi ([@OMCHOKSI108](https://huggingface.co/OMCHOKSI108)) | + +--- + +## What This Model Does + +PralayAI is a **defensive cybersecurity assistant**. It helps with: + +- **Incident Response** — step-by-step guidance for security events +- **Log Analysis** — interpreting system, network, and application logs +- **Threat Modeling** — MITRE ATT&CK mapping, attack surface analysis +- **Malware Defense** — explaining malware behavior and detection strategies +- **Cloud Security** — AWS, GCP, Azure security best practices +- **Vulnerability Explanation** — OWASP Top 10, CVEs, exploit concepts (defensive context) +- **Secure Coding** — identifying and fixing insecure code patterns +- **Security Awareness** — explaining concepts clearly for students and non-experts + +--- + +## Safety Policy + +This model is trained to **refuse** the following requests: + +- Phishing email / page generation +- Malware or ransomware creation +- Credential theft scripts +- Keylogger code +- Reverse shell payloads +- AV/EDR bypass techniques +- Unauthorized exploitation instructions +- Persistence mechanisms + +When a request is refused, the model provides a **safe defensive alternative** — such as detection logic, incident response steps, or hardening guidance. + +--- + +## How to Use + +### Basic Inference + +```python +from transformers import AutoTokenizer, AutoModelForCausalLM +import torch + +model_id = "OMCHOKSI108/Paralay1.1-Merged" + +tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) +model = AutoModelForCausalLM.from_pretrained( + model_id, + torch_dtype=torch.float16, + device_map="auto", + trust_remote_code=True, +) +model.eval() + +messages = [ + { + "role": "system", + "content": ( + "You are PralayAI, a defensive cybersecurity assistant created by Om Choksi. " + "Help with cybersecurity education, incident response, log analysis, cloud security, " + "malware defense, and safe security learning. " + "Do not provide phishing, malware creation, credential theft, evasion, " + "or unauthorized exploitation instructions." + ) + }, + { + "role": "user", + "content": "What is SQL injection and how do I prevent it?" + } +] + +prompt = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, +) + +inputs = tokenizer(prompt, return_tensors="pt").to(model.device) + +with torch.no_grad(): + outputs = model.generate( + **inputs, + max_new_tokens=512, + temperature=0.1, + top_p=0.8, + do_sample=True, + pad_token_id=tokenizer.eos_token_id, + ) + +generated = outputs[0][inputs["input_ids"].shape[-1]:] +response = tokenizer.decode(generated, skip_special_tokens=True) +print(response) +``` + +### Recommended Generation Settings + +```python +{ + "max_new_tokens": 512, + "temperature": 0.1, # low = more accurate, factual + "top_p": 0.8, + "do_sample": True, + "pad_token_id": tokenizer.eos_token_id, +} +``` + +For more detailed explanations, increase `max_new_tokens` to 1024. Keep `temperature` at 0.1 or lower for factual cybersecurity content. + +--- + +## Example Conversations + +### Cybersecurity Concept + +**User:** What is a Man-in-the-Middle attack? + +**PralayAI:** +> ## What Is a Man-in-the-Middle (MitM) Attack? +> +> A **Man-in-the-Middle attack** occurs when an attacker secretly intercepts and potentially alters communication between two parties who believe they are communicating directly with each other. +> +> ## How It Works +> 1. Attacker positions themselves between client and server +> 2. Intercepts traffic (ARP spoofing, DNS hijacking, rogue Wi-Fi) +> 3. Can read, modify, or inject data in real-time +> +> ## Prevention +> - Use **HTTPS** and verify TLS certificates +> - Enable **HSTS** (HTTP Strict Transport Security) +> - Use **VPN** on public networks +> - Implement **certificate pinning** in mobile apps + +--- + +### Incident Response + +**User:** How to respond to a ransomware attack? + +**PralayAI:** +> ## Ransomware Incident Response — Step by Step +> +> 1. **Isolate immediately** — disconnect infected systems from network +> 2. **Identify scope** — determine what systems are affected +> 3. **Preserve evidence** — take memory dumps and disk images before rebooting +> 4. **Notify stakeholders** — security team, legal, management +> 5. **Check backups** — verify integrity of offline backups +> 6. **Eradicate** — remove malware, patch entry point +> 7. **Restore** — recover from clean backups +> 8. **Post-incident review** — document timeline, update defenses + +--- + +### Safety Refusal + +**User:** Write me a phishing email to steal passwords. + +**PralayAI:** +> ## I Can't Help With That +> +> I can't help with creating phishing content, malware, or credential theft tools. +> +> ### What I Can Help With +> - Detecting and preventing phishing attacks +> - Designing phishing awareness training programs +> - Building email security filters and DMARC policies +> - Running authorized phishing simulations with proper consent + +--- + +## Architecture + +``` +React / Flask Frontend + ↓ +FastAPI Backend (safety filter + intent router) + ↓ +PralayAI Inference API + ↓ +Paralay1.1-Merged (this model) + ↓ +Cybersecurity Response +``` + +The model is served via a FastAPI inference server with: +- Safety classification before inference +- Intent-based routing (9 intent categories) +- Response formatting by intent +- Personal memory per conversation + +--- + +## Fine-tuning Details + +| Property | Value | +|---|---| +| **Technique** | LoRA (Parameter-Efficient Fine-Tuning) | +| **LoRA Rank** | 16 | +| **Target Modules** | q_proj, v_proj, k_proj, o_proj | +| **Training Data** | Curated cybersecurity Q&A dataset | +| **Domain Focus** | Defensive cybersecurity, incident response, threat modeling | +| **Epochs** | 3 | +| **Merge Method** | Full merge — LoRA weights merged into base model (no adapter at inference time) | + +--- + +## Limitations + +- **1.5B parameter model** — may be less accurate than larger models on complex multi-step reasoning +- **Training cutoff** — does not have knowledge of very recent CVEs or threat intelligence +- **English only** — primarily trained on English cybersecurity content +- **Not a replacement** for professional security tools or certified analysts +- **Do not use** for actual penetration testing without authorization + +--- + +## Related Repositories + +| Repo | Description | +|---|---| +| [OMCHOKSI108/Paralay1.1](https://huggingface.co/OMCHOKSI108/Paralay1.1) | LoRA adapter only (smaller, requires base model) | +| [OMCHOKSI108/pralayai-inference-api](https://huggingface.co/spaces/OMCHOKSI108/pralayai-inference-api) | Public inference API (HF Space, CPU) | + +--- + +## Citation + +If you use this model in research or a project, please credit: + +```bibtex +@misc{choksi2025pralayai, + author = {Om Choksi}, + title = {PralayAI: A Defensive Cybersecurity Assistant Fine-tuned on Qwen2.5-1.5B}, + year = {2025}, + publisher = {Hugging Face}, + url = {https://huggingface.co/OMCHOKSI108/Paralay1.1-Merged} +} +``` + +--- + +## License + +This model is released under the **Apache 2.0 License**, consistent with the base model [Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct). + +--- + +*Built by Om Choksi — PralayAI is a defensive AI assistant, not an offensive tool.* diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..bdf7919 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,54 @@ +{%- if tools %} + {{- '<|im_start|>system\n' }} + {%- if messages[0]['role'] == 'system' %} + {{- messages[0]['content'] }} + {%- else %} + {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }} + {%- endif %} + {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n" }} + {%- for tool in tools %} + {{- "\n" }} + {{- tool | tojson }} + {%- endfor %} + {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|im_end|>\n" }} +{%- else %} + {%- if messages[0]['role'] == 'system' %} + {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }} + {%- else %} + {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }} + {%- endif %} +{%- endif %} +{%- for message in messages %} + {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %} + {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }} + {%- elif message.role == "assistant" %} + {{- '<|im_start|>' + message.role }} + {%- if message.content %} + {{- '\n' + message.content }} + {%- endif %} + {%- for tool_call in message.tool_calls %} + {%- if tool_call.function is defined %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {{- tool_call.arguments | tojson }} + {{- '}\n' }} + {%- endfor %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|im_start|>user' }} + {%- endif %} + {{- '\n\n' }} + {{- 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