Instructions to use Marco711/Weather-R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Marco711/Weather-R1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Marco711/Weather-R1")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Marco711/Weather-R1", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Marco711/Weather-R1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Marco711/Weather-R1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Marco711/Weather-R1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Marco711/Weather-R1
- SGLang
How to use Marco711/Weather-R1 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 "Marco711/Weather-R1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Marco711/Weather-R1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "Marco711/Weather-R1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Marco711/Weather-R1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Marco711/Weather-R1 with Docker Model Runner:
docker model run hf.co/Marco711/Weather-R1
File size: 1,730 Bytes
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"_valid_kwargs_names": [
"do_convert_rgb",
"do_resize",
"size",
"size_divisor",
"default_to_square",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_pad",
"do_center_crop",
"crop_size",
"data_format",
"input_data_format",
"device",
"min_pixels",
"max_pixels",
"patch_size",
"temporal_patch_size",
"merge_size"
],
"crop_size": null,
"data_format": "channels_first",
"default_to_square": true,
"device": null,
"do_center_crop": null,
"do_convert_rgb": true,
"do_normalize": true,
"do_pad": null,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.48145466,
0.4578275,
0.40821073
],
"image_processor_type": "Qwen2VLImageProcessor",
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"input_data_format": null,
"max_pixels": 12845056,
"merge_size": 2,
"min_pixels": 3136,
"model_valid_processing_keys": [
"do_convert_rgb",
"do_resize",
"size",
"size_divisor",
"default_to_square",
"resample",
"do_rescale",
"rescale_factor",
"do_normalize",
"image_mean",
"image_std",
"do_pad",
"do_center_crop",
"crop_size",
"data_format",
"input_data_format",
"device",
"min_pixels",
"max_pixels",
"patch_size",
"temporal_patch_size",
"merge_size"
],
"patch_size": 14,
"processor_class": "Qwen2_5_VLProcessor",
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"longest_edge": 12845056,
"shortest_edge": 3136
},
"size_divisor": null,
"temporal_patch_size": 2,
"video_processor_type": "Qwen2VLVideoProcessor"
}
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