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

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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](https://huggingface.co/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](https://huggingface.co/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

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

	### Official Qwen vision template

	```bash
	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.