Instructions to use mjf-su/TheGarbageMan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mjf-su/TheGarbageMan with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-VL-4B-Instruct")
model = PeftModel.from_pretrained(base_model, "mjf-su/TheGarbageMan")

Transformers

How to use mjf-su/TheGarbageMan with Transformers:

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

pipe = pipeline("text-generation", model="mjf-su/TheGarbageMan")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mjf-su/TheGarbageMan", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mjf-su/TheGarbageMan with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mjf-su/TheGarbageMan"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mjf-su/TheGarbageMan",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mjf-su/TheGarbageMan

SGLang

How to use mjf-su/TheGarbageMan 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 "mjf-su/TheGarbageMan" \
    --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": "mjf-su/TheGarbageMan",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "mjf-su/TheGarbageMan" \
        --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": "mjf-su/TheGarbageMan",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mjf-su/TheGarbageMan with Docker Model Runner:
```
docker model run hf.co/mjf-su/TheGarbageMan
```

TheGarbageMan

File size: 782 Bytes

e00121f

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  "crop_size": null,
  "data_format": "channels_first",
  "default_to_square": true,
  "device": null,
  "disable_grouping": null,
  "do_center_crop": null,
  "do_convert_rgb": true,
  "do_normalize": true,
  "do_pad": null,
  "do_rescale": true,
  "do_resize": true,
  "image_mean": [
    0.5,
    0.5,
    0.5
  ],
  "image_processor_type": "Qwen2VLImageProcessorFast",
  "image_std": [
    0.5,
    0.5,
    0.5
  ],
  "input_data_format": null,
  "max_pixels": null,
  "merge_size": 2,
  "min_pixels": null,
  "pad_size": null,
  "patch_size": 16,
  "processor_class": "Qwen3VLProcessor",
  "resample": 3,
  "rescale_factor": 0.00392156862745098,
  "return_tensors": null,
  "size": {
    "longest_edge": 16777216,
    "shortest_edge": 65536
  },
  "temporal_patch_size": 2
}