Instructions to use Jiunsong/SuperQwen-AgentWorld-35B-A3B-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jiunsong/SuperQwen-AgentWorld-35B-A3B-abliterated with Transformers:

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

pipe = pipeline("text-generation", model="Jiunsong/SuperQwen-AgentWorld-35B-A3B-abliterated")
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("Jiunsong/SuperQwen-AgentWorld-35B-A3B-abliterated")
model = AutoModelForMultimodalLM.from_pretrained("Jiunsong/SuperQwen-AgentWorld-35B-A3B-abliterated", 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 Jiunsong/SuperQwen-AgentWorld-35B-A3B-abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jiunsong/SuperQwen-AgentWorld-35B-A3B-abliterated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jiunsong/SuperQwen-AgentWorld-35B-A3B-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jiunsong/SuperQwen-AgentWorld-35B-A3B-abliterated

SGLang

How to use Jiunsong/SuperQwen-AgentWorld-35B-A3B-abliterated 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 "Jiunsong/SuperQwen-AgentWorld-35B-A3B-abliterated" \
    --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": "Jiunsong/SuperQwen-AgentWorld-35B-A3B-abliterated",
		"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 "Jiunsong/SuperQwen-AgentWorld-35B-A3B-abliterated" \
        --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": "Jiunsong/SuperQwen-AgentWorld-35B-A3B-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jiunsong/SuperQwen-AgentWorld-35B-A3B-abliterated with Docker Model Runner:
```
docker model run hf.co/Jiunsong/SuperQwen-AgentWorld-35B-A3B-abliterated
```

SuperQwen-AgentWorld-35B-A3B-abliterated

File size: 1,250 Bytes

2ec47e2

{
  "bos_token_id": 248044,
  "do_sample": true,
  "eos_token_id": [
    248046,
    248044
  ],
  "pad_token_id": 248044,
  "bad_words_ids": [
    [
      248068
    ],
    [
      248069
    ],
    [
      90700,
      8340
    ],
    [
      81266,
      8340
    ],
    [
      25725,
      25
    ],
    [
      24342,
      286,
      25
    ],
    [
      18267,
      314,
      34022
    ],
    [
      8553,
      314,
      3272
    ],
    [
      8553,
      8404,
      7324,
      2370
    ],
    [
      18770,
      4087,
      25
    ],
    [
      760,
      1156,
      579,
      1622
    ],
    [
      760,
      1156,
      1622
    ],
    [
      11553,
      11258,
      11782,
      314,
      31626
    ],
    [
      40,
      668,
      4370
    ],
    [
      7676,
      8594,
      290,
      29415,
      8503,
      29
    ],
    [
      510,
      91046,
      29415,
      8503,
      29
    ],
    [
      760,
      1156,
      6587
    ],
    [
      760,
      1156,
      682
    ],
    [
      9764,
      579
    ],
    [
      13784,
      11
    ]
  ],
  "no_repeat_ngram_size": 12,
  "repetition_penalty": 1.05,
  "temperature": 0.6,
  "top_k": 20,
  "top_p": 0.95,
  "transformers_version": "5.12.1"
}