Instructions to use us-7z/20260815-195606 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use us-7z/20260815-195606 with Transformers:

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

pipe = pipeline("text-generation", model="us-7z/20260815-195606")
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("us-7z/20260815-195606")
model = AutoModelForMultimodalLM.from_pretrained("us-7z/20260815-195606", 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 us-7z/20260815-195606 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "us-7z/20260815-195606"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "us-7z/20260815-195606",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/us-7z/20260815-195606

SGLang

How to use us-7z/20260815-195606 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 "us-7z/20260815-195606" \
    --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": "us-7z/20260815-195606",
		"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 "us-7z/20260815-195606" \
        --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": "us-7z/20260815-195606",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use us-7z/20260815-195606 with Docker Model Runner:
```
docker model run hf.co/us-7z/20260815-195606
```

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Affine R334 — marsplan Online-DPO-long (merged)

LoRA-merged Online-DPO (β=0.1 α=32 r=16 G=4 lr=5e-6 temp=1.2 max_steps=300 FORCE_PREFIX) from live reign-22 king marsplan0624/affine-5gedzafcvg-queen@556d02a2.

Local n80 vs marsplan: margin=+0.01182 SE=0.00484 z=2.445 clears max(2·SE, δ=0.002); median_len_z=217; B pass=0.468. Stage-5 candidate.

Downloads last month: -

Safetensors

Model size

35B params

Tensor type

BF16

Model tree for us-7z/20260815-195606

Base model

marsplan0624/affine-5gedzafcvg-queen

Finetuned

(15)

this model