Instructions to use prajjwal1/ctrl_discovery_7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prajjwal1/ctrl_discovery_7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="prajjwal1/ctrl_discovery_7")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("prajjwal1/ctrl_discovery_7") model = AutoModelForCausalLM.from_pretrained("prajjwal1/ctrl_discovery_7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use prajjwal1/ctrl_discovery_7 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prajjwal1/ctrl_discovery_7" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prajjwal1/ctrl_discovery_7", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/prajjwal1/ctrl_discovery_7
- SGLang
How to use prajjwal1/ctrl_discovery_7 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 "prajjwal1/ctrl_discovery_7" \ --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": "prajjwal1/ctrl_discovery_7", "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 "prajjwal1/ctrl_discovery_7" \ --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": "prajjwal1/ctrl_discovery_7", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use prajjwal1/ctrl_discovery_7 with Docker Model Runner:
docker model run hf.co/prajjwal1/ctrl_discovery_7
Download pytorch_model.bin from prajjwal1/ctrl_discovery_7: direct link, hf CLI and curl.
- Browser
- Download file 6.55 GB
-
https://huggingface.co/prajjwal1/ctrl_discovery_7/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://prajjwal1/ctrl_discovery_7/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/prajjwal1/ctrl_discovery_7/resolve/main/pytorch_model.bin
6.55 GB
- Xet hash:
- bc2dba64b4e96307a9bf2637398cb5f07b523b5d08ed1199346edde31d9b6888
- Size of remote file:
- 6.55 GB
- SHA256:
- 4d15a373c929aece299206e8034096f2f9ddc1404365b27fdf4cddbce2acf467
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