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Model: OMCHOKSI108/Paralay1.1-Merged
Source: Original Platform

.gitattributes

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README.md

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+---
+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.*

chat_template.jinja

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+{%- 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 <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|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<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "float16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.12.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

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generation_config.json

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+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
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+  "pad_token_id": 151643,
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.12.0"
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