Instructions to use redflash6/sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use redflash6/sft with Transformers:

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

pipe = pipeline("image-text-to-text", model="redflash6/sft")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("redflash6/sft")
model = AutoModelForMultimodalLM.from_pretrained("redflash6/sft", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use redflash6/sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "redflash6/sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "redflash6/sft",
		"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/redflash6/sft

SGLang

How to use redflash6/sft 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 "redflash6/sft" \
    --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": "redflash6/sft",
		"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 "redflash6/sft" \
        --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": "redflash6/sft",
		"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 redflash6/sft with Docker Model Runner:
```
docker model run hf.co/redflash6/sft
```

REEL — sft

Project page · Code · Paper

REEL is a tool-using visual-memory planner for long-horizon, multi-shot video generation, built on Qwen3.5-9B. It retrieves and creates visual evidence, assesses generated candidates, and updates memory for later shots.

Checkpoint

Stage: Supervised fine-tuning.
Parent checkpoint: Qwen/Qwen3.5-9B.
Stored precision: float32, in Hugging Face Safetensors format.

This release contains the planner, tokenizer, processor configuration, and chat template. Video generators, optimizer states, and training data are not included. The GRPO releases use BF16 inference exports of their selected training checkpoints.

Use

Use a Transformers version with Qwen3_5ForConditionalGeneration support:

import torch
from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration

model_id = "redflash6/sft"
processor = AutoProcessor.from_pretrained(model_id)
model = Qwen3_5ForConditionalGeneration.from_pretrained(
    model_id, dtype=torch.bfloat16, device_map="auto"
)

This loads the planner, not an end-to-end video generator. Use the REEL tool runtime and backend adapters to execute memory actions and connect separately deployed video-generation services.

Released stages

SFT · Single-shot GRPO · Multi-shot GRPO

Citation

@misc{xu2026reel,
  title = {REEL: Learning to Retrieve, Evaluate, and Evolve Visual Memory for Any Long-Horizon Multi-Shot Video Generation},
  author = {Hang Xu and Guohui Zhang and Xiaoxiao Ma and Hu Yu and Jie Huang and Jiayin Xiao and Lin Song and Haoyang Huang and Nan Duan and Feng Zhao},
  year = {2026},
  note = {Preprint},
  url = {https://xuhang07.github.io/REEL/}
}

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Safetensors

Model size

9B params

Tensor type

F32

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