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