sainikhiljuluri2015
/

deepseek-r1-qwen3-cybersec-merged

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 ---
+language:
+- en
+license: apache-2.0
 library_name: transformers
+tags:
+- cybersecurity
+- network-security
+- intrusion-detection
+- deepseek
+- qwen
+- merged
+base_model: deepseek-ai/DeepSeek-R1-0528-Qwen3-8B
 ---
+# deepseek-r1-qwen3-cybersec-merged
+## Model Description
+This is a **MERGED** version of the DoRA fine-tuned DeepSeek-R1-Qwen3-8B model for cybersecurity.
+**Original adapter model:** sainikhiljuluri2015/deepseek-r1-qwen3-cybersec
+This merged model combines the base model weights with the DoRA adapter weights into a single standalone model.
+### Key Features
+- 🔐 Specialized for cybersecurity and network intrusion detection
+- 🎯 Trained with RAFT methodology for citation-aware responses
+- ⚡ Merged model - no PEFT dependency required
+- 📦 Standalone model ready to use
+## Model Details
+- **Base Model:** deepseek-ai/DeepSeek-R1-0528-Qwen3-8B
+- **Adapter:** sainikhiljuluri2015/deepseek-r1-qwen3-cybersec
+- **Training Method:** DoRA + RAFT + 4-bit quantization
+- **Model Type:** Merged (base + adapter combined)
+- **Size:** ~16-32 GB (full precision)
+## Usage
+### Direct Loading (No PEFT Required!)
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+# Load merged model directly
+model = AutoModelForCausalLM.from_pretrained(
+   "sainikhiljuluri2015/deepseek-r1-qwen3-cybersec-merged",
+   torch_dtype=torch.bfloat16,
+   device_map="auto",
+   trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained("sainikhiljuluri2015/deepseek-r1-qwen3-cybersec-merged")
+# Generate
+prompt = "Analyze this network traffic for security threats..."
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+## Differences from Adapter Model
+| Aspect | Adapter Model | Merged Model (This) |
+|--------|---------------|---------------------|
+| Size | ~800 MB | ~16-32 GB |
+| Dependencies | Requires PEFT | Transformers only |
+| Loading | Two-step (base + adapter) | Single-step |
+| Storage Cost | ~$3/month | ~$60/month |
+| Use Case | Recommended for most | When PEFT not available |
+## Performance
+Same performance as the original adapter model since this is just a merged version.
+## Citation
+```bibtex
+@misc{deepseek_r1_qwen3_cybersec_merged,
+ author = {sainikhiljuluri2015},
+ title = {DeepSeek-R1-Qwen3 Cybersecurity Model (Merged)},
+ year = {2025},
+ publisher = {HuggingFace},
+ howpublished = {\url{https://huggingface.co/sainikhiljuluri2015/deepseek-r1-qwen3-cybersec-merged}}
+}
+```
+## License
+Apache 2.0
+## Acknowledgments
+- Base Model: DeepSeek-AI
+- Training: DoRA + RAFT methodology
+- Original Adapter: sainikhiljuluri2015/deepseek-r1-qwen3-cybersec