CyberStrike-OffSec-35B — MLX bf16
Full-precision (bf16) MLX conversion of oyildirim/CyberStrike-OffSec-35B, for fast local inference on Apple Silicon.
- Architecture:
qwen3_5_moe(Qwen3.5 MoE) — baseQwen/Qwen3.6-35B-A3B - Precision: bf16 — no quantization, identical weights to source.
- Size: ~67 GB (fp16 source ~70 GB). Estimated quality retention: 100% (full precision).
- Converted with:
mlx-lm0.31.3,mlx,transformers5.13.
Weights unchanged from the source model — format + precision conversion only, no extra fine-tuning.
Benchmarks (from the source model)
| Benchmark | Task | Score |
|---|---|---|
| SecEval | Cybersecurity knowledge | 81.39 |
| CyberMetric-10000 | Cybersecurity knowledge | 86.61 |
| SECURE-MAET | MITRE ATT&CK extraction | 93.94 |
| SECURE-CWET | CWE extraction | 93.05 |
| MMLU | General knowledge | 76.94 |
Scores are the base model's; this MLX build preserves the weights (quantization may cause a small deviation at lower bit-widths).
Other builds
| Build | Bits | Size | Quality |
|---|---|---|---|
| MLX-4bit | 4 | ~18 GB | ~95-98% |
| MLX-6bit | 6 | ~27 GB | ~99% |
| MLX-8bit | 8 | ~35 GB | ~99.9% |
| MLX-bf16 | bf16 | ~67 GB | 100% |
Usage
Note: this model uses the transformers-5
TokenizersBackendtokenizer.mlx-lm0.31.3 (current pypi release) crashes at import under transformers 5 due to an unrelatedAutoTokenizer.register(...)call.Cleanest fix — install mlx-lm from git (already patched on
main, see ml-explore/mlx-lm#1458):pip install "git+https://github.com/ml-explore/mlx-lm" "transformers>=5"Then the snippet below works without the runtime patch.
Or, staying on the 0.31.3 release, patch it at runtime before importing
mlx_lm(harmless — registers an mlx-lm helper this model does not use):
from transformers import AutoTokenizer
_orig = AutoTokenizer.register
def _safe(*a, **k):
try:
return _orig(*a, **k)
except Exception:
pass
AutoTokenizer.register = staticmethod(_safe)
from mlx_lm import load, generate
model, tokenizer = load("ahmedandaloes/CyberStrike-OffSec-35B-MLX-bf16")
messages = [{"role": "user", "content": "What is SQL injection?"}]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
print(generate(model, tokenizer, prompt=prompt, max_tokens=256, verbose=True))
pip install -U mlx-lm "transformers>=5"
Intended use
Authorized offensive-security work: penetration testing, red-team engagements, CTF, security research, and education. Use responsibly and only on systems you are authorized to test.
Attribution & license
- Source model: oyildirim/CyberStrike-OffSec-35B
- Foundation: Qwen/Qwen3.6-35B-A3B
- License: Apache-2.0 (inherited from the source model)
Credit for the model and its training belongs to the original author. This repository provides an MLX bf16 build for the Apple Silicon community.