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nico248000000000/Qwen3.8-27B-Uncensored-FP8-nuclei

sourceHugging Faceotherupdated 1mo agoView on Hugging Face
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Qwen3.8-27B-Uncensored-FP8-nuclei

Instruction-tuned nuclei assistant to generate from CVE / exploit a nuclei script

Base model`orcarouter/Qwen3.8-27B-Uncensored-FP8`
Domainnuclei
MethodLoRA / QLoRA (Unsloth) · rank 8 · α 16
Quantization at trainbf16 LoRA
Context2048 tokens
Datasetdataset_nuclei.jsonl · train 919 / eval 10
GPUNVIDIA RTX PRO 6000 Blackwell Server Edition (95.0 GiB)
Wall time16.7 min
Modalities keptvision, video

LoRA/QLoRA fine-tune of the base model on a nuclei SFT dataset.

What changed vs the reference

Reference = the published base checkpoint `orcarouter/Qwen3.8-27B-Uncensored-FP8`, plus the first in-run loss (LoRA ≈ 0 at step 0).

MetricReference (base / first log)This fine-tuneΔ
Train loss (first → last logged)14.351114.2714-0.6%
Train loss (best)—14.1145—
Eval loss (holdout, first → last)14.090714.0907+0.0%

The first logged train loss is the closest in-run proxy for the base model (LoRA starts near zero). Option F, when executed, adds an independent holdout comparison against the frozen merged base.

Training data

  • —File: dataset_nuclei.jsonl
  • —Split: 0.01 holdout, seed 42
  • —Format: chat-templated SFT (messages / instruction+output / ### Instruction + ### Response)

Training procedure

HyperparameterValue
Epochs1
Learning rate0.0002
Warmup ratio0.05
Device batch4
Grad accum2
Effective batch8
Optimadamw_8bit
PackingTrue
LoRA targets['q_proj', 'k_proj', 'v_proj', 'o_proj', 'out_proj', 'gate_proj', 'up_proj', 'down_proj']

Intended use

Domain Q&A and drafting in the training domain.

Out of scope: Anything outside the training domain or that requires certification.

Multimodal

Kept towers: vision, video. Vision/audio layers were frozen during text SFT (vision=False, audio=False). Load the merged Transformers folder (or GGUF + mmproj) to keep image / video / audio.

How to use

python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "nico248000000000/Qwen3.8-27B-Uncensored-FP8-nuclei"
tok = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True,
)
messages = [{"role": "user", "content": 'How do you design a reliable Zero Trust infrastructure? Answer with a concrete control list.'}]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
print(tok.decode(model.generate(inputs, max_new_tokens=256)[0], skip_special_tokens=True))

Limitations

  • —Domain shift: quality drops outside the SFT topics.
  • —Eval above is holdout loss (and optional targeted checks). It is not a public leaderboard.
  • —The base model license and acceptable-use policy still apply.

License

other — inherit and respect the license of orcarouter/Qwen3.8-27B-Uncensored-FP8.