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Model: sabari2005/cyberslm-33m-instruct
Source: Original Platform

README.md

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+---
+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.