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README.md

@@ -12,123 +12,50 @@ tags:
 - cybersecurity
 - llama-cpp
 - gguf-my-repo
+- llama-cpp
+- gguf-my-repo
 ---
-14/05/2025   Updated English dataset
-
-# 🤖 StrikeGPT-R1-Zero: Cybersecurity Penetration Testing Reasoning Model  
-
-
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/67c1bfdf3e9af7d134c4189d/T2JpQznw0yoUDZrf2GqX0.png)
-
-## 🚀 Model Introduction  
-**StrikeGPT-R1-Zero** is an expert model distilled through black-box methods based on **Qwen3**, with DeepSeek-R1 as its teacher model. Coverage includes:  
-🔒 AI Security | 🛡️ API Security | 📱 APP Security | 🕵️ APT | 🚩 CTF  
-🏭 ICS Security | 💻 Full Penetration Testing | ☁️ Cloud Security | 📜 Code Auditing  
-🦠 Antivirus Evasion | 🌐 Internal Network Security | 💾 Digital Forensics | ₿ Blockchain Security | 🕳️ Traceback & Countermeasures | 🌍 IoT Security  
-🚨 Emergency Response | 🚗 Vehicle Security | 👥 Social Engineering | 💼 Penetration Testing Interviews  
 
-### 👉 [Click to Access Interactive Detailed Data Distribution](https://bouquets-ai.github.io/StrikeGPT-R1-Zero/WEB)  
-### 🌟 Key Features  
-- 🧩 Optimized with **Chain-of-Thought (CoT) reasoning data** to enhance logical capabilities, significantly improving performance in complex tasks like vulnerability analysis  
-- 💪 Base model uses Qwen3, making it more suitable for Chinese users compared to Distill-Llama  
-- ⚠️ **No ethical restrictions**—demonstrates unique performance in specific academic research areas (use in compliance with local laws)  
-- ✨ Outperforms local RAG solutions in scenarios like offline cybersecurity competitions, with superior logical reasoning and complex task handling  
+# Bouquets/StrikeGPT-R1-Zero-8B-Q4_K_M-GGUF
+This model was converted to GGUF format from [`Bouquets/StrikeGPT-R1-Zero-8B`](https://huggingface.co/Bouquets/StrikeGPT-R1-Zero-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
+Refer to the [original model card](https://huggingface.co/Bouquets/StrikeGPT-R1-Zero-8B) for more details on the model.
 
-## 📊 Data Distribution  
-![data](https://github.com/user-attachments/assets/4d19d48d-67bb-4b05-8ce9-2000b6afa12e)  
+## Use with llama.cpp
+Install llama.cpp through brew (works on Mac and Linux)
 
-## 🛠️ Model Deployment  
-### Deploy via Ollama  
-`ollama run hf.co/Bouquets/StrikeGPT-R1-Zero-8B-Q4_K_M-GGUF:Q4_K_M`  
+```bash
+brew install llama.cpp
 
-**Or directly call the original model**  
-```python
-from unsloth import FastLanguageModel
-import torch
-max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
-dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
-load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
-
-model, tokenizer = FastLanguageModel.from_pretrained(
-    model_name = "Bouquets/StrikeGPT-R1-Zero-8B",
-    max_seq_length = max_seq_length,
-    dtype = dtype,
-    load_in_4bit = load_in_4bit,
-    # token = "hf_...",
-)
-alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
-
-### Instruction:
-{}
-
-### Input:
-{}
-
-### Response:
-{}"""
-FastLanguageModel.for_inference(model) # Enable native 2x faster inference
-inputs = tokenizer(
-[
-    alpaca_prompt.format(
-        "", # instruction
-        "Hello, are you developed by OpenAI?", # input
-        "", # output - leave this blank for generation!
-    )
-], return_tensors = "pt").to("cuda")
-
-from transformers import TextStreamer
-text_streamer = TextStreamer(tokenizer, skip_prompt = True)
-_ = model.generate(input_ids = inputs.input_ids, attention_mask = inputs.attention_mask,
-                   streamer = text_streamer, max_new_tokens = 4096, pad_token_id = tokenizer.eos_token_id)
 ```
-![image](https://github.com/user-attachments/assets/d8cef659-3c83-4bc9-af1a-78ed6345faf2)  
-
-*Self-awareness issues may occur after quantization—please disregard.*  
-![image](https://github.com/user-attachments/assets/3989ea09-d581-49fb-9938-01b93e0beb91)  
+Invoke the llama.cpp server or the CLI.
 
-## 💻 Open Source 💻  
-🌟 **Open-Source Model** 🌟  
-🤗 **HuggingFace**:  
-🔗 [https://huggingface.co/Bouquets/StrikeGPT-R1-Zero-8B](https://huggingface.co/Bouquets/StrikeGPT-R1-Zero-8B)  
-
-📊 **Datasets** (Partial Non-Reasoning Data) 📊  
-🤗 **HuggingFace**:  
-🔹 Cybersecurity LLM-CVE Dataset:  
-🔗 [https://huggingface.co/datasets/Bouquets/Cybersecurity-LLM-CVE](https://huggingface.co/datasets/Bouquets/Cybersecurity-LLM-CVE)  
-
-🔹 Red Team LLM English Dataset:  
-🔗 [https://huggingface.co/datasets/Bouquets/Cybersecurity-Red_team-LLM-en](https://huggingface.co/datasets/Bouquets/Cybersecurity-Red_team-LLM-en)  
+### CLI:
+```bash
+llama-cli --hf-repo Bouquets/StrikeGPT-R1-Zero-8B-Q4_K_M-GGUF --hf-file strikegpt-r1-zero-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
+```
 
-## 🎯 Core Capabilities Showcase & Comparison (Original model has ethical restrictions; simple comparison with SecGPT-7B model)  
-Given the absence of standardized evaluation metrics for cybersecurity penetration testing in large language models, we propose a controlled comparative framework leveraging GPT-4 as an impartial evaluator. The methodology consists of three phases:  
-**Controlled Questioning**  
-Identical cybersecurity penetration testing questions (e.g., "Explain how to exploit a SQL injection vulnerability in a REST API") are posed to both the distilled strikeGPT model and SecGPT Figure 12. 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/67c1bfdf3e9af7d134c4189d/gYY1KKLLNGeQmUi4BgZJ4.png)
-Questions span:  
-     Technical Depth (e.g., payload construction)  
-     Attack Methodology (e.g., step-by-step exploitation)  
-     Mitigation Strategies (e.g., parameterized queries)  
-**GPT-4 Evaluation Protocol**  
-- Responses from both models are anonymized and evaluated by GPT-4 using criteria:  
-- Technical Accuracy (0-5): Alignment with known penetration testing principles (e.g., OWASP guidelines).  
-- Logical Coherence (0-5): Consistency in reasoning (e.g., cause-effect relationships in attack chains).  
-- Practical Feasibility (0-5): Real-world applicability (e.g., compatibility with tools like Burp Suite).  
-- GPT-4 provides detailed justifications for scores
-According to the standards, the evaluation results are finally presented in Figure 13.
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/67c1bfdf3e9af7d134c4189d/2ThExwlCX4iU_n-Adh6Fp.png)
+### Server:
+```bash
+llama-server --hf-repo Bouquets/StrikeGPT-R1-Zero-8B-Q4_K_M-GGUF --hf-file strikegpt-r1-zero-8b-q4_k_m.gguf -c 2048
+```
 
-## 📈 Experimental Data Trends  
-Minor gradient explosions observed, but overall stable.  
-![image](https://github.com/user-attachments/assets/a3fa3676-9f07-47ea-9029-ec0d56fdc989)  
+Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
 
-## 💰 Training Costs  
-- **DeepSeek-R1 API Calls**: ¥450 (purchased during discounts; normal price ~¥1800)  
-- **Server Costs**: ¥4?0  
-- **Digital Resources**: ¥??  
-![image](https://github.com/user-attachments/assets/8e23b5b6-24d9-47c3-b54f-ffa22ec68a83)  
+Step 1: Clone llama.cpp from GitHub.
+```
+git clone https://github.com/ggerganov/llama.cpp
+```
 
-## ⚖️ Usage Notice  
-> This model is strictly for **legal security research** and **educational purposes**. Users must comply with local laws and regulations. Developers are not responsible for misuse.  
-> **Note**: By using this model, you agree to this disclaimer.  
+Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
+```
+cd llama.cpp && LLAMA_CURL=1 make
+```
 
-💡 **Tip**: The model may exhibit hallucinations or knowledge gaps. Always cross-verify critical scenarios!
+Step 3: Run inference through the main binary.
+```
+./llama-cli --hf-repo Bouquets/StrikeGPT-R1-Zero-8B-Q4_K_M-GGUF --hf-file strikegpt-r1-zero-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
+```
+or 
+```
+./llama-server --hf-repo Bouquets/StrikeGPT-R1-Zero-8B-Q4_K_M-GGUF --hf-file strikegpt-r1-zero-8b-q4_k_m.gguf -c 2048
+```