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 / README.md

cbert33

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base_model: DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored
license: apache-2.0
library_name: transformers
pipeline_tag: image-text-to-text
tags:
  - qwen3_8
  - multimodal
  - compressed-tensors
  - fp8
  - speculative-decoding
  - uncensored
language:
  - en

Qwen3.8 27B TWIN-TURBO Fable Cold Fusion 709-L Uncensored, FP8 + Calibrated FP8 KV

This is a calibrated FP8 derivative of DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored. The source checkpoint was pinned at revision 9cdc928b322203a58c362584882105a56918a640.

The text model uses block-scaled FP8 weights and dynamic FP8 activations. Calibration also produced static tensor-wise FP8 KV-cache scales. The vision tower and restored MTP weights retain their source precision.

Quantization

Toolchain: LLM Compressor 0.13.0, compressed-tensors 0.18.0, Transformers 5.13.1, PyTorch 2.11.0+cu130
Text weights: FP8, 128 x 128 block scaling
Activations: dynamic FP8, group size 128
KV cache: FP8 with static tensor-wise calibrated scales
Calibration data: 512 UltraChat samples, maximum sequence length 2,048
Preserved from the source: vision tower, MTP, embeddings, LM head, and linear-attention state projections
Format: Transformers safetensors with compressed-tensors metadata

The exact serialized recipe is included in recipe.yaml.

Chat templates

The release includes both the source model's custom template and the official Qwen template. Neither template was edited.

DavidAU template: default text and tool path

chat_template.jinja is the exact template from the pinned DavidAU source revision. chat_template-davidau-original.jinja is an identical named copy. The source's other template variants are also included unchanged.

The DavidAU template preserves the model's custom TWIN-TURBO reasoning and tool controls. Text generation passed validation with this template.

Official Qwen template: vision path

chat_template-qwen-original.jinja is the exact official template from Qwen/Qwen3.8-27B, pinned at revision 1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0.

During validation, the DavidAU template rejected a user image with System message cannot contain images. The official Qwen template accepted the same image request and returned the correct answer. Use the official template when serving vision requests.

vLLM examples

DavidAU text and tool template

vllm serve <MODEL_ID> \
  --quantization compressed-tensors \
  --kv-cache-dtype fp8 \
  --speculative-config '{"method":"mtp","num_speculative_tokens":2}'

Official Qwen vision template

vllm serve <MODEL_ID> \
  --quantization compressed-tensors \
  --kv-cache-dtype fp8 \
  --speculative-config '{"method":"mtp","num_speculative_tokens":2}' \
  --chat-template chat_template-qwen-original.jinja \
  --limit-mm-per-prompt.image 1

Set context length, concurrency, and memory allocation for the serving hardware. The commands above show the template and quantization-specific options only.

Validation

Static checks:

all 3 safetensor files and all 1,631 indexed tensors were readable and fully covered by the index;
all 333 vision tensors exactly matched the source checkpoint;
all 15 MTP tensors exactly matched the source checkpoint;
146 other protected tensors exactly matched the source checkpoint;
400 eligible text weights were FP8 and had 400 matching scale tensors;
tokenizer and image/video processor contracts matched the source.

Runtime checks used vLLM 0.29.0 with FlashInfer 0.6.18:

the multimodal target, calibrated FP8 KV cache, and MTP loaded successfully;
the DavidAU template returned QUANT_OK over the text path with HTTP 200;
the official Qwen template identified a solid red image as red with HTTP 200;
MTP accepted tokens at both configured draft positions;
no request error, queue wait, restart, or OOM occurred in the successful validation runs.

These are loading and canary checks, not a quality benchmark. Quantization can change model quality.

Source model and license

The model behavior, training claims, and uncensoring method come from the DavidAU source model. Its pinned model card is preserved as README.upstream.md.

This derivative follows the source model's Apache 2.0 license. Review the source model card and license terms before use.