Instructions to use sjin59/c43g-s600m2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sjin59/c43g-s600m2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="sjin59/c43g-s600m2")
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("sjin59/c43g-s600m2")
model = AutoModelForMultimodalLM.from_pretrained("sjin59/c43g-s600m2", 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 sjin59/c43g-s600m2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sjin59/c43g-s600m2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sjin59/c43g-s600m2",
		"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/sjin59/c43g-s600m2

SGLang

How to use sjin59/c43g-s600m2 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 "sjin59/c43g-s600m2" \
    --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": "sjin59/c43g-s600m2",
		"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 "sjin59/c43g-s600m2" \
        --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": "sjin59/c43g-s600m2",
		"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 sjin59/c43g-s600m2 with Docker Model Runner:
```
docker model run hf.co/sjin59/c43g-s600m2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

c43g-s600m2

Given 4 shuffled frames from a video plus a caption describing what happens, this model recovers the original chronological order of the frames.

Fine-tuned from Qwen3.5-27B (VLM):

Qwen3.5-27B
  → non-thinking full SFT   (16,070 rows, seed 43, 1,004 steps)
  → non-thinking GRPO LoRA  (r32/α64, 1,400 steps; checkpoint-600 selected)
  → LoRA merge              = this model

Output is four letters in chronological order, e.g. "C,B,A,D".

Usage

Runs on vLLM 0.19.0. In bf16 it needs 2× H100 80GB (TP2). For single-GPU inference use the GGUF build, which fits on one RTX 3090 24GB.

Default sampling is temperature 0.6 / top_p 0.95 / top_k 20 — not greedy decoding.

Licensed under Apache-2.0, following the base model Qwen3.5-27B.

Downloads last month: 8

Safetensors

Model size

27B params

Tensor type

BF16

Model tree for sjin59/c43g-s600m2

Base model

Qwen/Qwen3.5-27B

Finetuned

(344)

this model

Quantizations

1 model