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sabari2005
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Model Details
Model Provider
sabari2005
Model Tree
sabari2005/cyberslm-33m-base
Input Modalities
Output Modalities
Supported Functionality
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.
The model was finetuned with this template (built into tokenizer.chat_template):
text
<s>### User:{question}### Assistant:{answer}</s>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. Useapply_chat_templateand you don't need to think about it.
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.
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))