Instructions to use HumorR1/policy-e1b-sft-thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HumorR1/policy-e1b-sft-thinking with Transformers:

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

pipe = pipeline("image-text-to-text", model="HumorR1/policy-e1b-sft-thinking")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("HumorR1/policy-e1b-sft-thinking")
model = AutoModelForImageTextToText.from_pretrained("HumorR1/policy-e1b-sft-thinking")
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

vLLM

How to use HumorR1/policy-e1b-sft-thinking with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HumorR1/policy-e1b-sft-thinking"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HumorR1/policy-e1b-sft-thinking",
		"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/HumorR1/policy-e1b-sft-thinking

SGLang

How to use HumorR1/policy-e1b-sft-thinking 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 "HumorR1/policy-e1b-sft-thinking" \
    --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": "HumorR1/policy-e1b-sft-thinking",
		"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 "HumorR1/policy-e1b-sft-thinking" \
        --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": "HumorR1/policy-e1b-sft-thinking",
		"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 HumorR1/policy-e1b-sft-thinking with Docker Model Runner:
```
docker model run hf.co/HumorR1/policy-e1b-sft-thinking
```

policy-e1b-sft-thinking / README.md

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metadata

license: apache-2.0
base_model: Qwen/Qwen3-VL-2B-Thinking
library_name: transformers
tags:
  - vision-language
  - new-yorker
  - humor
  - rlhf
  - sft-thinking
datasets:
  - yguooo/newyorker_caption_ranking
language:
  - en

humor-r1 — SFT, with thinking (Qwen3-VL-2B-Thinking + LoRA, merged) (E1b)

LoRA-adapted Qwen3-VL-2B-Thinking supervised fine-tuned on (image, synthetic thinking, chosen caption) triples, then merged. Output format: {thinking}</think>\n\n<caption>X</caption>.

Training data

271 New Yorker contests, top-rated caption per contest (yguooo/newyorker_caption_ranking).
The 60k Bradley-Terry preference pairs underlying the reward model (separate split).
We deliberately do NOT use the dataset's GPT-4o-generated Scene/Twist/Location/Entities descriptions in the prompt, since they hand-feed scene content to a vision-language model that can already see the image; this makes the policy and reward model usable on any single-panel cartoon, not just the curated subset.

How it fits the project

Part of a 2x2 ablation over training method (SFT, GRPO) and output format (no thinking, thinking) for humor caption generation. See HumorR1/rm-qwen25vl-3b-nodesc for the reward model used to train (and score) this policy.

Inference

Backbone: Qwen/Qwen3-VL-2B-Thinking. This repo is a merged full model; load with transformers.AutoModelForCausalLM.from_pretrained.

from PIL import Image
from transformers import AutoProcessor
from vllm import LLM, SamplingParams


processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-2B-Thinking", trust_remote_code=True)
llm = LLM(model="Qwen/Qwen3-VL-2B-Thinking", trust_remote_code=True, dtype="bfloat16",
          max_model_len=4096)

# Caption format: <caption>X</caption>; thinking variant prefixes <think>...</think>.

Reward model used during training

HumorR1/rm-qwen25vl-3b-nodesc (held-out pairwise accuracy 0.6635).