Instructions to use pachequinho/qwen2.5-d2g-scheme-medium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use pachequinho/qwen2.5-d2g-scheme-medium with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct") model = PeftModel.from_pretrained(base_model, "pachequinho/qwen2.5-d2g-scheme-medium") - Transformers
How to use pachequinho/qwen2.5-d2g-scheme-medium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="pachequinho/qwen2.5-d2g-scheme-medium") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("pachequinho/qwen2.5-d2g-scheme-medium", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use pachequinho/qwen2.5-d2g-scheme-medium with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "pachequinho/qwen2.5-d2g-scheme-medium" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pachequinho/qwen2.5-d2g-scheme-medium", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/pachequinho/qwen2.5-d2g-scheme-medium
- SGLang
How to use pachequinho/qwen2.5-d2g-scheme-medium 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 "pachequinho/qwen2.5-d2g-scheme-medium" \ --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": "pachequinho/qwen2.5-d2g-scheme-medium", "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 "pachequinho/qwen2.5-d2g-scheme-medium" \ --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": "pachequinho/qwen2.5-d2g-scheme-medium", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use pachequinho/qwen2.5-d2g-scheme-medium with Docker Model Runner:
docker model run hf.co/pachequinho/qwen2.5-d2g-scheme-medium
File size: 1,528 Bytes
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"image_processor": {
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"image_processor_type": "Qwen2VLImageProcessorFast",
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"size": {
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"shortest_edge": 3136
},
"temporal_patch_size": 2
},
"processor_class": "Qwen2_5_VLProcessor",
"video_processor": {
"data_format": "channels_first",
"default_to_square": true,
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"do_sample_frames": false,
"image_mean": [
0.48145466,
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],
"image_processor_type": "Qwen2VLImageProcessor",
"image_std": [
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],
"max_frames": 768,
"max_pixels": 12845056,
"merge_size": 2,
"min_frames": 4,
"min_pixels": 3136,
"patch_size": 14,
"resample": 3,
"rescale_factor": 0.00392156862745098,
"return_metadata": false,
"size": {
"longest_edge": 12845056,
"shortest_edge": 3136
},
"temporal_patch_size": 2,
"video_processor_type": "Qwen2VLVideoProcessor"
}
}
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