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

Libraries

How to use DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-ULTRA-HERETIC-Uncensored with Transformers:

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

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

Install from pip and serve model

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

SGLang

How to use DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-ULTRA-HERETIC-Uncensored 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 "DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-ULTRA-HERETIC-Uncensored" \
    --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": "DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-ULTRA-HERETIC-Uncensored",
		"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 "DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-ULTRA-HERETIC-Uncensored" \
        --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": "DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-ULTRA-HERETIC-Uncensored",
		"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 DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-ULTRA-HERETIC-Uncensored with Docker Model Runner:
```
docker model run hf.co/DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-ULTRA-HERETIC-Uncensored
```

DavidAU commited on 18 days ago

Commit

e13094d

verified ·

1 Parent(s): 3cfa555

Upload chat_template-tturbo-v2.jinja

Browse files

Files changed (1) hide show

chat_template-tturbo-v2.jinja +3 -2

chat_template-tturbo-v2.jinja CHANGED Viewed

@@ -225,7 +225,8 @@ Operating rules:
   {% set raw_reason = "" %}
   {% set txt = render_content(msg.content, false) | trim %}
   {% if (msg.role == 'user' and "{REASON:" in txt) %}
-  {% set candidate = txt.split("{REASON:")[1].split("}")[0] if (msg.role == 'user' and "{REASON:" in txt) else "" %}
   {% set candidate_key = candidate[1:] if candidate.startswith('i') else candidate %}
   {% if candidate and candidate_key in ('xhigh', 'medium', 'low', 'einstein', 'spoon') %}
     {% set raw_reason = candidate %}
@@ -246,13 +247,13 @@ Operating rules:
     {% endif %}
   {% endif %}
 {% endfor %}
-{% set messages = store.messages %}
 {% if store.reasoning_effort %}
   {% set reasoning_effort = store.reasoning_effort %}
   {% set enable_thinking = store.enable_thinking %}
   {% set ns_state.effort = store.reasoning_effort %}
   {% set ns_state.thinking = store.enable_thinking %}
 {% endif %}
 {%- set reasoning_instructions = '' %}
 {%- if enable_thinking is undefined or enable_thinking is true %}
     {%- set resolved_reasoning_effort = reasoning_effort|default('xhigh') %}

   {% set raw_reason = "" %}
   {% set txt = render_content(msg.content, false) | trim %}
   {% if (msg.role == 'user' and "{REASON:" in txt) %}
+  {% set after_tag = txt.split("{REASON:") %}
+  {% set candidate = after_tag[1].split("}")[0] %}
   {% set candidate_key = candidate[1:] if candidate.startswith('i') else candidate %}
   {% if candidate and candidate_key in ('xhigh', 'medium', 'low', 'einstein', 'spoon') %}
     {% set raw_reason = candidate %}
     {% endif %}
   {% endif %}
 {% endfor %}
 {% if store.reasoning_effort %}
   {% set reasoning_effort = store.reasoning_effort %}
   {% set enable_thinking = store.enable_thinking %}
   {% set ns_state.effort = store.reasoning_effort %}
   {% set ns_state.thinking = store.enable_thinking %}
 {% endif %}
+{% set messages = store.messages %}
 {%- set reasoning_instructions = '' %}
 {%- if enable_thinking is undefined or enable_thinking is true %}
     {%- set resolved_reasoning_effort = reasoning_effort|default('xhigh') %}