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 16

Commit

1ea70ec

1 Parent(s): 10081c6

Update README.md

Browse files

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README.md +17 -3

README.md CHANGED Viewed

@@ -17,7 +17,7 @@ library_name: transformers
 STATUS:
 - IT IS COOKING... #1 done, now in testing [test results/generation(s) below ]] || initial benches posted ("NEEDLE": MOVED) ...
-- 2nd cook in progress, to assess / refine detail levels in reasoning/output.
 - More details below.
 <B>COLD FUSION? This model uses the COLD FUSION (GAIN+Unsloth) fine tuning and training methods as noted here:</b>
@@ -56,7 +56,8 @@ and "Grand Intelligence" (9 stages)) versions and the newest (soon to be release
 <B>Timeline for "Qwen3.8-27B-Cold-Fusion-GAIN-V1.1":</b>
 - DONE: Primary cook[s]. ; 1st complete ; primary testing next. || maybe additional "cook(s)".
 - DONE: BENCHES: Primary cook, Qwen 3.8 27B non trained, and 3.5/3.6 to compare...
-- IN PROGRESS: Second "cook" to compare reasoning adjustments and detail levels in thinking/output and REFINE if required.
 - 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).
@@ -85,7 +86,7 @@ NOTES:
 ---
-Important note on 3.8 bench VS Qwen 3.6/3.5 versions:
 Based on my testing / Qwen's own statements, community statements (ie localllama) and extended benchs for 3.8-27B the 3.8 version (team Qwen) this model is more focused on
 deeper thinking, coding and agentic functions than previous Qwen versions.
@@ -127,6 +128,10 @@ NOTE: Some formatting may be lost on copy/paste/export.
 ---
 <style type="text/css">
 		@page { size: 21cm 29.7cm; margin: 2cm }
 		p { line-height: 115%; margin-bottom: 0.25cm; background: transparent }
@@ -1321,3 +1326,12 @@ represent longer-term, higher-impact but more challenging solutions.</span></p>
 </p>

 STATUS:
 - IT IS COOKING... #1 done, now in testing [test results/generation(s) below ]] || initial benches posted ("NEEDLE": MOVED) ...
+- 2nd cook in progress, to assess / refine detail levels in reasoning/output. Cook #2 complete. In testing/benching. Test generation to be posted.
 - More details below.
 <B>COLD FUSION? This model uses the COLD FUSION (GAIN+Unsloth) fine tuning and training methods as noted here:</b>
 <B>Timeline for "Qwen3.8-27B-Cold-Fusion-GAIN-V1.1":</b>
 - DONE: Primary cook[s]. ; 1st complete ; primary testing next. || maybe additional "cook(s)".
 - DONE: BENCHES: Primary cook, Qwen 3.8 27B non trained, and 3.5/3.6 to compare...
+- DONE: Second "cook" to compare reasoning adjustments and detail levels in thinking/output and REFINE if required.
+- IN PROGRESS: Assessment of "cook #2", testing, and comparing to build 1/untrained Qwen 3.8-27B
 - 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).
 ---
+Important note on Qwen 27B 3.8 bench VS Qwen 3.6/3.5 27B versions:
 Based on my testing / Qwen's own statements, community statements (ie localllama) and extended benchs for 3.8-27B the 3.8 version (team Qwen) this model is more focused on
 deeper thinking, coding and agentic functions than previous Qwen versions.
 ---
+Example Generation is from BUILD/COOK #1.
+---
 <style type="text/css">
 		@page { size: 21cm 29.7cm; margin: 2cm }
 		p { line-height: 115%; margin-bottom: 0.25cm; background: transparent }
 </p>
+---
+Example Generation 2 is from BUILD/COOK #2.
+Same prompt/settings.
+---
+[to be added]