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

Libraries

How to use COinCO/Context_Classification_Models with Transformers:

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

pipe = pipeline("image-text-to-text", model="COinCO/Context_Classification_Models")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("COinCO/Context_Classification_Models", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use COinCO/Context_Classification_Models with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "COinCO/Context_Classification_Models"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "COinCO/Context_Classification_Models",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/COinCO/Context_Classification_Models

SGLang

How to use COinCO/Context_Classification_Models 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 "COinCO/Context_Classification_Models" \
    --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": "COinCO/Context_Classification_Models",
		"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 "COinCO/Context_Classification_Models" \
        --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": "COinCO/Context_Classification_Models",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use COinCO/Context_Classification_Models with Docker Model Runner:
```
docker model run hf.co/COinCO/Context_Classification_Models
```

Context_Classification_Models / location /chat_template.jinja

ruitongs

Upload folder using huggingface_hub

bf5ba1e verified 2 months ago

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	{% endif %}<\|im_start\|>{{ message['role'] }}
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	{% endif %}

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	You are a helpful assistant.<\|im_end\|>
	{% endif %}<\|im_start\|>{{ message['role'] }}
	{% if message['content'] is string %}{{ message['content'] }}<\|im_end\|>
	{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<\|vision_start\|><\|image_pad\|><\|vision_end\|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<\|vision_start\|><\|video_pad\|><\|vision_end\|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<\|im_end\|>
	{% endif %}{% endfor %}{% if add_generation_prompt %}<\|im_start\|>assistant
	{% endif %}