Instructions to use sabari2005/cyberslm-33m-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sabari2005/cyberslm-33m-instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="sabari2005/cyberslm-33m-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sabari2005/cyberslm-33m-instruct")
model = AutoModelForCausalLM.from_pretrained("sabari2005/cyberslm-33m-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sabari2005/cyberslm-33m-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sabari2005/cyberslm-33m-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sabari2005/cyberslm-33m-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sabari2005/cyberslm-33m-instruct

SGLang

How to use sabari2005/cyberslm-33m-instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "sabari2005/cyberslm-33m-instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sabari2005/cyberslm-33m-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "sabari2005/cyberslm-33m-instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sabari2005/cyberslm-33m-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sabari2005/cyberslm-33m-instruct with Docker Model Runner:
```
docker model run hf.co/sabari2005/cyberslm-33m-instruct
```

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. 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.

Downloads last month: -

Safetensors

Model size

33.5M params

Tensor type

F32

Model tree for sabari2005/cyberslm-33m-instruct

Base model

sabari2005/cyberslm-33m-base

Finetuned

(1)

this model