---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
library_name: transformers
base_model: sabari2005/cyberslm-33m-base
tags:
- cybersecurity
- small-language-model
- instruct
- sft
- llama
---

# CyberSLM-33M-Instruct

Instruction-tuned version of
[`cyberslm-33m-base`](https://huggingface.co/sabari2005/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

```python
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. Use `apply_chat_template` and 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.