Instructions to use DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1 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-Cold-Fusion-GAIN-V1.1")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1")
model = AutoModelForMultimodalLM.from_pretrained("DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1 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-Cold-Fusion-GAIN-V1.1"
# 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-Cold-Fusion-GAIN-V1.1",
		"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-Cold-Fusion-GAIN-V1.1

SGLang

How to use DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1 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-Cold-Fusion-GAIN-V1.1" \
    --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-Cold-Fusion-GAIN-V1.1",
		"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-Cold-Fusion-GAIN-V1.1" \
        --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-Cold-Fusion-GAIN-V1.1",
		"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-Cold-Fusion-GAIN-V1.1 with Docker Model Runner:
```
docker model run hf.co/DavidAU/Qwen3.8-27B-Cold-Fusion-GAIN-V1.1
```

DavidAU commited on Aug 18

Commit

8f6a33b

verified ·

1 Parent(s): 3bfdfd4

Update README.md

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README.md +7 -14

README.md CHANGED Viewed

@@ -86,12 +86,11 @@ and "Grand Intelligence" (9 stages)) versions and the newest (soon to be release
 - DONE: Second "cook" to compare reasoning adjustments and detail levels in thinking/output and REFINE if required.
 - DONE: Assessment of "cook #2" / 2B, testing, and comparing to build 1/untrained Qwen 3.8-27B
 - DONE: Cook 1B to address/test issues related from jinja reasoning injection related to "xhigh" / "system prompt"
-- Assessing Cook 1 and 1B / 2B ; finals ; next step final tweaking/GGUF release.
-- IN PROGRESS: Additional "HERETIC" version (from BASE, Qwen 3.8 untrained model) being built then will be trained too.
-- IN PROGRESS: Multiple checkpoints, benching and human testing.
-- Adjustment(s).
-- GGUF repo(s).
-- Release of source.
 <B>Prelim Testing Results (training #1, CP #1) // "1B":</b>
@@ -115,14 +114,6 @@ NOTES:
 - 3 generations each for both models, same prompt to assess function.
 - Thinking block/output is assessed in terms of function and quality - especially detail level, and language.
-<B>Prelim Testing Results (training #2B, CP #1):</b>
-- Adjustments to jinja to address system prompt injection (xhigh) affecting training.
-- Reasoning : Full retention of detail, thinking/output 1/3 to 1/2 normal Qwen size.
-- Reasoning is clearer, well laid out, and little to no "hesitations"
-More to come.
 ---
 <h2>BENCHMARKS by Nightmedia</h2>
@@ -177,6 +168,8 @@ NOTE: Some formatting may be lost on copy/paste/export.
 Example Generation is from BUILD/COOK #1.
 ---
 <style type="text/css">

 - DONE: Second "cook" to compare reasoning adjustments and detail levels in thinking/output and REFINE if required.
 - DONE: Assessment of "cook #2" / 2B, testing, and comparing to build 1/untrained Qwen 3.8-27B
 - DONE: Cook 1B to address/test issues related from jinja reasoning injection related to "xhigh" / "system prompt"
+- DONE: Assessing Cook 1 and 1B / 2B ; finals ; next step final tweaking/GGUF release.
+- DONE: Adjustment(s).
+- DONE: GGUF repo(s).
+- DONE: Release of source.
+- IN PROGRESS: Additional "HERETIC" version (from BASE, Qwen 3.8 untrained model) being built then will be trained too. This will be addressed in future version(s).
 <B>Prelim Testing Results (training #1, CP #1) // "1B":</b>
 - 3 generations each for both models, same prompt to assess function.
 - Thinking block/output is assessed in terms of function and quality - especially detail level, and language.
 ---
 <h2>BENCHMARKS by Nightmedia</h2>
 Example Generation is from BUILD/COOK #1.
+For examples of COOK 1B - the final winner - see the above "GGUF" repo.
 ---
 <style type="text/css">