An English LFM2.5 2.6B checkpoint associated with the Trendyol Cybersecurity Instruction Tuning Dataset. This repository contains both Transformers-format model files and local GGUF exports.
This is a research release. The repository does not currently publish benchmark, baseline-comparison, or safety-evaluation results.
Lineage
Repository formats
Transformers assets include model.safetensors, configuration files, tokenizer files, and a chat template.
GGUF exports include:
Keeping both formats in one repository is convenient, but users should explicitly choose the path that matches their runtime.
Transformers usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "reaperdoesntknow/LFM2.5-2.6B-CyberSec"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
)
messages = [
{"role": "user", "content": "Explain defense in depth in plain language."}
]
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=256)
answer = outputs[0][inputs["input_ids"].shape[-1]:]
print(tokenizer.decode(answer, skip_special_tokens=True))
The checked configuration includes a bitsandbytes quantization block. Pin and test the exact Transformers, Accelerate, bitsandbytes, and device environment you intend to use.
GGUF usage
With a recent llama.cpp build:
llama-cli \
-hf reaperdoesntknow/LFM2.5-2.6B-CyberSec:Q4_K_M \
--jinja
With Ollama:
ollama run hf.co/reaperdoesntknow/LFM2.5-2.6B-CyberSec:Q4_K_M
Intended use
- Research on small-model responses to cybersecurity instruction prompts.
- Local qualitative testing and format comparison.
- Comparison with the unchanged LiquidAI base model.
- Evaluation-harness and inference-runtime development.
Evaluation status
The dataset tag and exported files are observed. Improved cybersecurity ability is not established by those facts alone.
Evidence needed for a stronger release claim includes:
- A held-out test split and unchanged-base baseline.
- Named cybersecurity and general-capability benchmarks.
- Reproducible harness, seed, prompts, and model revision hashes.
- Safety, misuse, and hallucination evaluation.
- Separate results for the Transformers model and each GGUF quantization.
Limitations and safety
- The model can produce incorrect, outdated, insecure, or harmful instructions.
- Cybersecurity material is inherently dual use.
- The public files reviewed for this card do not document preprocessing, contamination checks, full training hyperparameters, or checkpoint-selection criteria.
- Quantized builds can behave differently from the Transformers checkpoint.
- Do not execute generated commands without review and isolation.
- Do not use this model as the sole basis for incident response, vulnerability disclosure, access control, or other consequential decisions.
Part of the CIx cybersecurity model collection.
