Downloads · 30 days
88
11% of all-time downloads
ahmedandaloes/CyberStrike-OffSec-35B-MLX-6bit
CyberStrike-OffSec-35B-MLX-6bit is a text generation model from ahmedandaloes. Use it when you need the model to write or continue text. It is set up for mlx. The card lists the license as apache-2.0.
6-bit quantized MLX conversion of oyildirim/CyberStrike-OffSec-35B, for fast local inference on Apple Silicon.
Downloads · 30 days
88
11% of all-time downloads
All-time downloads
775
Public
Parameters
34.7B
28.2 GB on disk
Likes
1
Public
Click a slice to open those files.
.safetensors28.2 GB · 100%
How the weights are stored.
U3234.7B · 100%
From the Hugging Face model README
6-bit quantized MLX conversion of oyildirim/CyberStrike-OffSec-35B, for fast local inference on Apple Silicon.
qwen3_5_moe (Qwen3.5 MoE) — base Qwen/Qwen3.6-35B-A3Bmlp.gate, mlp.shared_expert_gate) kept at 8-bit to preserve routing quality.mlx-lm 0.31.3, mlx, transformers 5.13.Weights unchanged from the source model — format + precision conversion only, no extra fine-tuning.
| 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).
| 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% |
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-6bit")
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"
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.
Credit for the model and its training belongs to the original author. This repository provides an MLX 6-bit build for the Apple Silicon community.