Instructions to use cbert33/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-FP8-Calibrated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cbert33/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-FP8-Calibrated with Transformers:

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

pipe = pipeline("image-text-to-text", model="cbert33/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-FP8-Calibrated")
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("cbert33/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-FP8-Calibrated")
model = AutoModelForMultimodalLM.from_pretrained("cbert33/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-FP8-Calibrated", 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 cbert33/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-FP8-Calibrated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cbert33/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-FP8-Calibrated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cbert33/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-FP8-Calibrated",
		"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/cbert33/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-FP8-Calibrated

SGLang

How to use cbert33/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-FP8-Calibrated 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 "cbert33/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-FP8-Calibrated" \
    --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": "cbert33/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-FP8-Calibrated",
		"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 "cbert33/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-FP8-Calibrated" \
        --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": "cbert33/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-FP8-Calibrated",
		"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 Desktop
Docker Model Runner
How to use cbert33/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-FP8-Calibrated with Docker Model Runner:
```
docker model run hf.co/cbert33/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-FP8-Calibrated
```

Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-FP8-Calibrated / VALIDATION.json

cbert33

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	{
	"artifact_status": "validated",
	"source": {
	"repo_id": "DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored",
	"revision": "9cdc928b322203a58c362584882105a56918a640",
	"license": "apache-2.0"
	},
	"quantization": {
	"llmcompressor": "0.13.0",
	"compressed_tensors": "0.18.0",
	"transformers": "5.13.1",
	"torch": "2.11.0+cu130",
	"weight_scheme": "FP8_BLOCK",
	"weight_block": [128, 128],
	"activation_scheme": "dynamic FP8",
	"kv_cache_scheme": "static calibrated tensor-wise FP8",
	"calibration_dataset": "ultrachat-200k",
	"calibration_samples": 512,
	"calibration_max_sequence_length": 2048
	},
	"static_validation": {
	"safetensor_files": 3,
	"indexed_tensors": 1631,
	"vision_tensors_exact": 333,
	"mtp_tensors_exact": 15,
	"other_protected_tensors_exact": 146,
	"fp8_text_weights": 400,
	"fp8_scale_tensors": 400
	},
	"templates": {
	"default": {
	"file": "chat_template.jinja",
	"source": "DavidAU pinned source revision",
	"sha256": "4c8895bb94b082b677d6709fa0fcb197a3db0149de4d3e99fa63446d3c7df9df",
	"validated_for": ["text"]
	},
	"davidau_named_copy": {
	"file": "chat_template-davidau-original.jinja",
	"sha256": "4c8895bb94b082b677d6709fa0fcb197a3db0149de4d3e99fa63446d3c7df9df"
	},
	"official_qwen": {
	"file": "chat_template-qwen-original.jinja",
	"repo_id": "Qwen/Qwen3.8-27B",
	"revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
	"sha256": "c3cf9e34abf4f9e36c2d72165aa9c132d3e2a725b6c2586aaa3a8af9d7a81041",
	"validated_for": ["vision"]
	}
	},
	"runtime_validation": {
	"vllm": "0.29.0",
	"flashinfer": "0.6.18",
	"text_template": "chat_template.jinja",
	"text_http_status": 200,
	"text_expected": "QUANT_OK",
	"text_observed": "QUANT_OK",
	"vision_template": "chat_template-qwen-original.jinja",
	"vision_http_status": 200,
	"vision_expected": "red",
	"vision_observed": "red",
	"mtp_draft_positions_exercised": 2,
	"request_errors": 0,
	"queue_waits": 0,
	"restarts": 0,
	"ooms": 0
	}
	}