Instructions to use HaiderK/Assignment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HaiderK/Assignment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="HaiderK/Assignment")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("HaiderK/Assignment") model = AutoModelForMultimodalLM.from_pretrained("HaiderK/Assignment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use HaiderK/Assignment with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HaiderK/Assignment" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HaiderK/Assignment", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HaiderK/Assignment
- SGLang
How to use HaiderK/Assignment 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 "HaiderK/Assignment" \ --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": "HaiderK/Assignment", "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 "HaiderK/Assignment" \ --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": "HaiderK/Assignment", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HaiderK/Assignment with Docker Model Runner:
docker model run hf.co/HaiderK/Assignment
Download training_args.bin from HaiderK/Assignment: direct link, hf CLI and curl.
- Browser
- Download file 4.22 kB
-
https://huggingface.co/HaiderK/Assignment/resolve/ea7cfd54e5dfd34f80cf12c4fc1270b61317fb93/training_args.bin
- Command line
-
hf download hf://HaiderK/Assignment@ea7cfd54e5dfd34f80cf12c4fc1270b61317fb93/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/HaiderK/Assignment/resolve/ea7cfd54e5dfd34f80cf12c4fc1270b61317fb93/training_args.bin
4.22 kB
- Xet hash:
- 8f18a254352901f255ee55e05b31d1558f626e0e3902ed9c5c18d1df0907bcd3
- Size of remote file:
- 4.22 kB
- SHA256:
- b852589469496db74210f991cd8e63b6cd18c490a7196d713d48deccbc04d062
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