Instructions to use EmiliaLee/31c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EmiliaLee/31c with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B")
model = PeftModel.from_pretrained(base_model, "EmiliaLee/31c")

Transformers

How to use EmiliaLee/31c with Transformers:

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

pipe = pipeline("text-generation", model="EmiliaLee/31c")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("EmiliaLee/31c", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use EmiliaLee/31c with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EmiliaLee/31c"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EmiliaLee/31c",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EmiliaLee/31c

SGLang

How to use EmiliaLee/31c 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 "EmiliaLee/31c" \
    --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": "EmiliaLee/31c",
		"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 "EmiliaLee/31c" \
        --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": "EmiliaLee/31c",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use EmiliaLee/31c with Docker Model Runner:
```
docker model run hf.co/EmiliaLee/31c
```

31c

File size: 1,191 Bytes

91cead1

{
  "image_processor": {
    "do_convert_rgb": true,
    "do_normalize": true,
    "do_rescale": true,
    "do_resize": true,
    "image_mean": [
      0.5,
      0.5,
      0.5
    ],
    "image_processor_type": "Qwen2VLImageProcessor",
    "image_std": [
      0.5,
      0.5,
      0.5
    ],
    "merge_size": 2,
    "patch_size": 16,
    "resample": 3,
    "rescale_factor": 0.00392156862745098,
    "size": {
      "longest_edge": 16777216,
      "shortest_edge": 65536
    },
    "temporal_patch_size": 2
  },
  "processor_class": "Qwen3VLProcessor",
  "video_processor": {
    "do_convert_rgb": true,
    "do_normalize": true,
    "do_rescale": true,
    "do_resize": true,
    "do_sample_frames": true,
    "fps": 2,
    "image_mean": [
      0.5,
      0.5,
      0.5
    ],
    "image_std": [
      0.5,
      0.5,
      0.5
    ],
    "max_frames": 768,
    "merge_size": 2,
    "min_frames": 4,
    "patch_size": 16,
    "resample": 3,
    "rescale_factor": 0.00392156862745098,
    "return_metadata": false,
    "size": {
      "longest_edge": 25165824,
      "shortest_edge": 4096
    },
    "temporal_patch_size": 2,
    "video_processor_type": "Qwen3VLVideoProcessor"
  }
}