Instructions to use Xingyu-Zheng/Qwopus3.5-9B-v3.5-INT4-FOEM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Local Apps Settings

How to use Xingyu-Zheng/Qwopus3.5-9B-v3.5-INT4-FOEM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Xingyu-Zheng/Qwopus3.5-9B-v3.5-INT4-FOEM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Xingyu-Zheng/Qwopus3.5-9B-v3.5-INT4-FOEM",
		"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/Xingyu-Zheng/Qwopus3.5-9B-v3.5-INT4-FOEM

SGLang

How to use Xingyu-Zheng/Qwopus3.5-9B-v3.5-INT4-FOEM 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 "Xingyu-Zheng/Qwopus3.5-9B-v3.5-INT4-FOEM" \
    --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": "Xingyu-Zheng/Qwopus3.5-9B-v3.5-INT4-FOEM",
		"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 "Xingyu-Zheng/Qwopus3.5-9B-v3.5-INT4-FOEM" \
        --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": "Xingyu-Zheng/Qwopus3.5-9B-v3.5-INT4-FOEM",
		"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 Xingyu-Zheng/Qwopus3.5-9B-v3.5-INT4-FOEM with Docker Model Runner:
```
docker model run hf.co/Xingyu-Zheng/Qwopus3.5-9B-v3.5-INT4-FOEM
```

Qwopus3.5-9B-v3.5-INT4-FOEM

File size: 1,231 Bytes

3ca7f22

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  "bits": 4,
  "group_size": 128,
  "desc_act": false,
  "lm_head": false,
  "method": "gptq",
  "quant_method": "gptq",
  "format": "gptq",
  "checkpoint_format": "gptq",
  "pack_dtype": "int32",
  "meta": {
    "quantizer": [
      "gptqmodel:6.1.0-dev"
    ],
    "uri": "https://github.com/modelcloud/gptqmodel",
    "damp_percent": 0.05,
    "damp_auto_increment": 0.01,
    "static_groups": false,
    "true_sequential": true,
    "mse": 0.0,
    "gptaq": null,
    "foem": {
      "alpha": 0,
      "beta": 0.2,
      "device": "auto"
    },
    "act_group_aware": true,
    "fallback": {
      "strategy": "rtn",
      "threshold": "0.5%",
      "smooth": null
    },
    "offload_to_disk": true,
    "offload_to_disk_path": "./gptqmodel_offload/sbzlavop-ptkwyiqe/",
    "pack_impl": "cpu",
    "gc_mode": "interval",
    "wait_for_submodule_finalizers": false,
    "auto_forward_data_parallel": true,
    "dense_vram_strategy": "exclusive",
    "dense_vram_strategy_devices": null,
    "moe_vram_strategy": "exclusive",
    "moe_vram_strategy_devices": null,
    "mock_quantization": false,
    "hessian": {
      "chunk_size": null,
      "chunk_bytes": null,
      "staging_dtype": "float32"
    }
  },
  "sym": true
}