Image-Text-to-Text
Transformers
Safetensors
qwen3_5
unsloth
GAIN Training
COLD-FUSION
finetune
conversational
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
- Transformers
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
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<b>IT IS COOKING [#1 done, now in testing] ... Using the GAIN/COLD FUSION fine tuning and training methods as noted here:</b>
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https://huggingface.co/DavidAU/Qwen3.6-27B-V1.1-FF711-Darker-Hero-GAIN-H2.0
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<B>Timeline for "Qwen3.8-27B-Cold-Fusion-GAIN-V1.1":</b>
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- Primary cook[s]. ; 1st complete ; primary testing next. || maybe additional "cook(s)".
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- Multiple checkpoints, benching and human testing.
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- Adjustment(s).
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- GGUF repo(s).
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- Release of source.
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<img src="cannonball.webp">
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<B>Prelim Testing Results (training #1, CP #1):</b>
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GENERATIONS from TESTING below.
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- Drastic reducing in thinking tokens / "caveman" talk.
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- Dropped to 1/10 to 1/2 number of tokens for thinking.
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- Zero "wait" and "hesitate"
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- Output is clean and organized.
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<b>IT IS COOKING [#1 done, now in testing] ... Using the GAIN/COLD FUSION fine tuning and training methods as noted here:</b>
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https://huggingface.co/DavidAU/Qwen3.6-27B-V1.1-FF711-Darker-Hero-GAIN-H2.0
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<B>Timeline for "Qwen3.8-27B-Cold-Fusion-GAIN-V1.1":</b>
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- Primary cook[s]. ; 1st complete ; primary testing next. || maybe additional "cook(s)".
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- Additional "HERETIC" version (from BASE, Qwen 3.8 untrained model) being built then will be trained too.
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- Multiple checkpoints, benching and human testing.
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- Adjustment(s).
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- GGUF repo(s).
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- Release of source.
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<h2>EXAMPLE GENERATIONS:</h2>
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Q4KS, non imatrix, standard Qwen settings, NO cache compression of any kind.
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NOTE: Some formatting may be lost on copy/paste/export.
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