File size: 1,670 Bytes
574d070 1eaa18d 574d070 1eaa18d 574d070 1eaa18d 574d070 1eaa18d 574d070 1eaa18d 574d070 1eaa18d 574d070 1eaa18d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 |
---
base_model: unsloth/DeepSeek-R1-Distill-Llama-8B
tags:
- text-generation
- transformers
- unsloth
- deepseek
- llama
- trl
license: apache-2.0
language:
- en
---
# DeepSeek Cybersecurity Model v1.1
This is a fine-tuned version of **Unsloth’s DeepSeek-R1-Distill-Llama-8B** model, adapted for cybersecurity-focused text generation tasks.
### Overview
- **Base model:** [`unsloth/DeepSeek-R1-Distill-Llama-8B`](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-8B)
- **Developer:** [yanmyoaung](https://huggingface.co/yanmyoaung)
- **Fine-tuned with:** [Unsloth](https://github.com/unslothai/unsloth) + Hugging Face [TRL](https://github.com/huggingface/transformers/tree/main/examples/research_projects/trl)
- **Merged Weights:** LoRA adapter merged into base model (16-bit)
- **License:** Apache 2.0
### Model Purpose
This model is optimized for generating and understanding cybersecurity-related content, such as:
- Threat intelligence summaries
- Vulnerability analysis
- Incident response suggestions
- Cybersecurity Q&A and explanation generation
### Inference Example
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("yanmyoaung04/deepseek-cybersecurity-model-v1.1")
tokenizer = AutoTokenizer.from_pretrained("yanmyoaung04/deepseek-cybersecurity-model-v1.1")
prompt = "Explain what a buffer overflow vulnerability is."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
### License
Apache 2.0 — free for academic and commercial use.
---
|