Instructions to use nico248000000000/Qwen3.8-27B-cyber with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nico248000000000/Qwen3.8-27B-cyber with Transformers:

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

pipe = pipeline("image-text-to-text", model="nico248000000000/Qwen3.8-27B-cyber")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("nico248000000000/Qwen3.8-27B-cyber")
model = AutoModelForMultimodalLM.from_pretrained("nico248000000000/Qwen3.8-27B-cyber", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nico248000000000/Qwen3.8-27B-cyber with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nico248000000000/Qwen3.8-27B-cyber"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nico248000000000/Qwen3.8-27B-cyber",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/nico248000000000/Qwen3.8-27B-cyber

SGLang

How to use nico248000000000/Qwen3.8-27B-cyber 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 "nico248000000000/Qwen3.8-27B-cyber" \
    --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": "nico248000000000/Qwen3.8-27B-cyber",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "nico248000000000/Qwen3.8-27B-cyber" \
        --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": "nico248000000000/Qwen3.8-27B-cyber",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Unsloth Studio

How to use nico248000000000/Qwen3.8-27B-cyber with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for nico248000000000/Qwen3.8-27B-cyber to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for nico248000000000/Qwen3.8-27B-cyber to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nico248000000000/Qwen3.8-27B-cyber to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="nico248000000000/Qwen3.8-27B-cyber",
    max_seq_length=2048,
)

Docker Model Runner
How to use nico248000000000/Qwen3.8-27B-cyber with Docker Model Runner:
```
docker model run hf.co/nico248000000000/Qwen3.8-27B-cyber
```

Qwen3.8-27B-cyber / README.md

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metadata

base_model: Qwen/Qwen3.8-27B
library_name: transformers
pipeline_tag: image-text-to-text
license: other
language:
  - en
  - fr
tags:
  - unsloth
  - lora
  - qlora
  - cyber
  - image-text-to-text
  - vision
  - video
model-index:
  - name: Qwen3.8-27B-cyber
    results:
      - task:
          type: text-generation
          name: Causal language modeling
        dataset:
          name: cyber SFT holdout
          type: dataset_cyber.jsonl
        metrics:
          - type: loss
            value: 0.741224
            name: eval_loss

Qwen3.8-27B-cyber

Instruction-tuned cybersecurity assistant (offensive, defensive, GRC, architecture, SOC/DFIR, RSSI).


Base model	`Qwen/Qwen3.8-27B`
Domain	cyber
Method	LoRA / QLoRA (Unsloth) · rank 8 · α 16
Quantization at train	bf16 LoRA
Context	2048 tokens
Dataset	`dataset_cyber.jsonl` · train 57718 / eval 584
GPU	NVIDIA RTX PRO 6000 Blackwell Server Edition (95.0 GiB)
Wall time	53.0 min
Modalities kept	vision, video

This checkpoint continues a strong general model and specialises it on a curated SFT corpus of cybersecurity procedures: pentest / red team, SOC and DFIR, cloud and identity, GRC (ISO, NIST, NIS2, DORA), and RSSI / project-management questions. Answers are meant to be concrete (controls, detections, hardening), not generic essays.

What changed vs the reference

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

Metric	Reference (base / first log)	This fine-tune	Δ
Train loss (first → last logged)	2.8025	0.1832	-93.5%
Train loss (best)	—	0.6627	—
Eval loss (holdout, first → last)	0.9783	0.7412	-24.2%

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_cyber.jsonl
Path used at train time: /content/drive/MyDrive/finetuning/dataset_cyber.jsonl
Split: 0.01 holdout, seed 42
Format: chat-templated SFT (messages / instruction+output / ### Instruction + ### Response)

Training procedure

Hyperparameter	Value
Epochs	1
Learning rate	0.0002
Warmup ratio	0.05
Device batch	4
Grad accum	2
Effective batch	8
Optim	adamw_8bit
Packing	True
LoRA targets	`['q_proj', 'k_proj', 'v_proj', 'o_proj', 'out_proj', 'gate_proj', 'up_proj', 'down_proj']`

Intended use

Authorized defensive work, tabletop exercises, control design, detection engineering, audit readiness, and explaining attack techniques without weaponized payloads.

Out of scope: Do not use it to attack systems you do not own, to generate exploit payloads, or as a substitute for a licensed auditor or incident commander.

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

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "nico248000000000/Qwen3.8-27B-cyber"
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 Qwen/Qwen3.8-27B.