Instructions to use LSW142857/Qwen3.5-9B-OPSD-iter32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LSW142857/Qwen3.5-9B-OPSD-iter32 with Transformers:

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

pipe = pipeline("image-text-to-text", model="LSW142857/Qwen3.5-9B-OPSD-iter32")
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("LSW142857/Qwen3.5-9B-OPSD-iter32")
model = AutoModelForMultimodalLM.from_pretrained("LSW142857/Qwen3.5-9B-OPSD-iter32", 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 LSW142857/Qwen3.5-9B-OPSD-iter32 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LSW142857/Qwen3.5-9B-OPSD-iter32"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LSW142857/Qwen3.5-9B-OPSD-iter32",
		"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/LSW142857/Qwen3.5-9B-OPSD-iter32

SGLang

How to use LSW142857/Qwen3.5-9B-OPSD-iter32 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 "LSW142857/Qwen3.5-9B-OPSD-iter32" \
    --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": "LSW142857/Qwen3.5-9B-OPSD-iter32",
		"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 "LSW142857/Qwen3.5-9B-OPSD-iter32" \
        --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": "LSW142857/Qwen3.5-9B-OPSD-iter32",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use LSW142857/Qwen3.5-9B-OPSD-iter32 with Docker Model Runner:
```
docker model run hf.co/LSW142857/Qwen3.5-9B-OPSD-iter32
```

Qwen3.5-9B OPSD iteration 32

This is the complete, merged Hugging Face model produced by applying the OPSD iteration 32 LoRA update to Qwen/Qwen3.5-9B. The repository contains the base model tensors, tokenizer, processor configuration, and model index. A separate base-model download is not required.

Download

hf download LSW142857/Qwen3.5-9B-OPSD-iter32 \
  --repo-type model \
  --local-dir Qwen3.5-9B-OPSD-iter32

The repository is public. The four safetensors shards are about 19.3 GB in total, so make sure the evaluation server has enough disk space and RAM/VRAM.

Run for evaluation

Use the downloaded directory as the model path. For example, with vLLM:

vllm serve ./Qwen3.5-9B-OPSD-iter32 \
  --tensor-parallel-size 1 \
  --max-model-len 262144 \
  --reasoning-parser qwen3

For text-only evaluation, add --language-model-only. SGLang and Hugging Face Transformers can likewise be pointed at ./Qwen3.5-9B-OPSD-iter32. Qwen3.5 support may require recent/nightly versions of those frameworks.

Merge provenance

Training job: 48054
Adapter checkpoint: checkpoint_iter_0000032
Adapter SHA256: 81c22bdc1d9328bb16c97483017f0570ed990cca61ec26b438d7a96e5b7557f7
LoRA scaling used during merge: alpha / rank = 128 / 64 = 2.0
Merged LoRA target tensors: 135
Merge output format: standard Hugging Face safetensors shards
Detailed file sizes, checksums, and provenance: MERGED_MODEL_MANIFEST.json

The original LoRA adapter remains available separately at LSW142857/OPSD-Qwen3.5-9B-LoRA-iter32. This repository is the self-contained evaluation artifact; it does not contain optimizer state or training data.

License and attribution

The base model license and notices are retained in LICENSE. Please follow the Qwen3.5 license and the terms of any downstream evaluation environment.

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Safetensors

Model size

10B params

Tensor type

BF16

F32

Model tree for LSW142857/Qwen3.5-9B-OPSD-iter32

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Qwen/Qwen3.5-9B-Base

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