Update README.md

README.md

@@ -1,5 +1,6 @@
 ---
-base_model: Bouquets/StrikeGPT-R1-Zero-8B
+base_model:
+- huihui-ai/Qwen3-8B-abliterated
 language:
 - en
 - zh
@@ -12,50 +13,123 @@ tags:
 - cybersecurity
 - llama-cpp
 - gguf-my-repo
-- llama-cpp
-- gguf-my-repo
 ---
+14/05/2025   Updated English dataset
 
-# 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.
+# 🤖 StrikeGPT-R1-Zero: Cybersecurity Penetration Testing Reasoning Model  
 
-## Use with llama.cpp
-Install llama.cpp through brew (works on Mac and Linux)
 
-```bash
-brew install llama.cpp
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/67c1bfdf3e9af7d134c4189d/T2JpQznw0yoUDZrf2GqX0.png)
 
-```
-Invoke the llama.cpp server or the CLI.
+## 🚀 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  
 
-### 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"
-```
+### 👉 [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  
 
-### 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
-```
+## 📊 Data Distribution  
+![data](https://github.com/user-attachments/assets/4d19d48d-67bb-4b05-8ce9-2000b6afa12e)  
 
-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.
+## 🛠️ Model Deployment  
+### Deploy via Ollama  
+`ollama run hf.co/Bouquets/StrikeGPT-R1-Zero-8B-Q4_K_M-GGUF:Q4_K_M`  
 
-Step 1: Clone llama.cpp from GitHub.
-```
-git clone https://github.com/ggerganov/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.
 
-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
-```
+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.
 
-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
+### 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)  
+
+## 💻 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)  
+
+## 🎯 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)
+
+## 📈 Experimental Data Trends  
+Minor gradient explosions observed, but overall stable.  
+![image](https://github.com/user-attachments/assets/a3fa3676-9f07-47ea-9029-ec0d56fdc989)  
+
+## 💰 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)  
+
+## ⚖️ 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.  
+
+💡 **Tip**: The model may exhibit hallucinations or knowledge gaps. Always cross-verify critical scenarios!