--- language: - en license: lgpl-3.0 tags: - text-generation - gpt-oss - cybersecurity - vllm base_model: unsloth/gpt-oss-20b-BF16 libraries: - transformers library_name: transformers model_type: gpt_oss pipeline_tag: text-generation datasets: - AlicanKiraz0/All-CVE-Records-Training-Dataset --- # CyberOSS-CVE # CyberOSS-CVE Fine-tuned `gpt-oss-20b` on the `AlicanKiraz0/All-CVE-Records-Training-Dataset` using Unsloth with LoRA (rank 32) and merged back to BF16 for compatibility with vLLM, Hugging Face Transformers, and GGUF conversions. ## Training Overview - **Base model**: `unsloth/gpt-oss-20b-BF16` - **Dataset**: `AlicanKiraz0/All-CVE-Records-Training-Dataset` - **Hardware**: single NVIDIA H100 80GB - **Sequence length**: 2048 - **Batch**: 2 (grad accum 4 → effective 8) - **Learning rate**: 2e-4, linear warmup 5 steps - **Steps**: 100 for quick verification run (expand for full epoch) - **Loss masking**: full conversation (system, user, assistant) ## Files - `model-0000X-of-00009.safetensors`: merged BF16 shards - `config.json`: GPT-OSS architecture config - `tokenizer.json` and template: Harmony/GPT-OSS chat format - `chat_template.jinja`: OpenAI Harmony-compatible chat template ## Quick Usage (vLLM) ```bash pip install vllm==0.11.2 transformers==4.57.2 python - <<'PY' from vllm import LLM, SamplingParams from transformers.processing_utils import ProcessorMixin import transformers transformers.ProcessorMixin = ProcessorMixin llm = LLM( model="Kushalkhemka/CyberOSS-CVE", tokenizer="unsloth/gpt-oss-20b-BF16", dtype="bfloat16", ) prompt = "You are a cybersecurity assistant. Summarize CVE-2010-3763." out = llm.generate([prompt], SamplingParams(max_tokens=128))[0] print(out.outputs[0].text) PY ``` ## HF Transformers ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("Kushalkhemka/CyberOSS-CVE", torch_dtype="bfloat16", device_map="auto") tokenizer = AutoTokenizer.from_pretrained("unsloth/gpt-oss-20b-BF16") ``` ## License Matches upstream `unsloth/gpt-oss-20b` (LGPL-3.0). Respect dataset terms when redistributing.