76 lines
2.3 KiB
Markdown
76 lines
2.3 KiB
Markdown
---
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license: apache-2.0
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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base_model: sabari2005/cyberslm-33m-base
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tags:
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- cybersecurity
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- small-language-model
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- instruct
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- sft
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- llama
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---
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# CyberSLM-33M-Instruct
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Instruction-tuned version of
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[`cyberslm-33m-base`](https://huggingface.co/sabari2005/cyberslm-33m-base) —
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a **33.5M-parameter cybersecurity-focused small language model** trained from
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scratch, then supervised-finetuned on **24,980 cybersecurity Q&A
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conversations** with loss masking on assistant tokens only.
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## Architecture
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Decoder-only transformer (Llama-style, loadable with `LlamaForCausalLM`):
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384 hidden / 12 layers / 6 heads / SwiGLU 1024 / RMSNorm / RoPE θ=10,000 /
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4096 context / 32k SentencePiece vocab / tied embeddings.
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Total: 33,531,264 parameters.
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## Chat format
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The model was finetuned with this template (built into `tokenizer.chat_template`):
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```
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<s>### User:
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{question}
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### Assistant:
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{answer}</s>
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```
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tok = AutoTokenizer.from_pretrained("sabari2005/cyberslm-33m-instruct")
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model = AutoModelForCausalLM.from_pretrained("sabari2005/cyberslm-33m-instruct")
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messages = [{"role": "user", "content": "Explain what a SQL injection attack is and how to prevent it."}]
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ids = tok.apply_chat_template(messages, add_generation_prompt=True,
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return_tensors="pt", add_special_tokens=False)
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out = model.generate(ids, max_new_tokens=256, do_sample=True,
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temperature=0.7, top_p=0.9, eos_token_id=3, pad_token_id=0)
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print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
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```
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> Note: the `<s>`/`</s>` markers in the template are literal text (the
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> SentencePiece vocab uses `<bos>`/`<eos>` pieces), matching exactly how the
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> model was trained. Use `apply_chat_template` and you don't need to think
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> about it.
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## Training
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- SFT on 24,980 cyber Q&A samples (multi-turn conversation format)
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- 3 epochs, LR 2e-5 cosine, AdamW β=(0.9, 0.95), wd 0.01, loss on assistant tokens only
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- Final val loss: **2.66**
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## Limitations
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33M parameters: strong at short cybersecurity explanations and Q&A; not
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suited for long-horizon reasoning, code generation, or general assistant
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duties. May hallucinate specifics (CVE numbers, tool flags) — verify facts.
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English only.
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