Instructions to use rovai/chatbotmedium2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rovai/chatbotmedium2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rovai/chatbotmedium2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("rovai/chatbotmedium2") model = AutoModelForCausalLM.from_pretrained("rovai/chatbotmedium2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use rovai/chatbotmedium2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rovai/chatbotmedium2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rovai/chatbotmedium2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/rovai/chatbotmedium2
- SGLang
How to use rovai/chatbotmedium2 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 "rovai/chatbotmedium2" \ --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": "rovai/chatbotmedium2", "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 "rovai/chatbotmedium2" \ --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": "rovai/chatbotmedium2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use rovai/chatbotmedium2 with Docker Model Runner:
docker model run hf.co/rovai/chatbotmedium2
Download training_args.bin from rovai/chatbotmedium2: direct link, hf CLI and curl.
- Browser
- Download file 1.33 kB
-
https://huggingface.co/rovai/chatbotmedium2/resolve/main/training_args.bin
- Command line
-
hf download hf://rovai/chatbotmedium2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rovai/chatbotmedium2/resolve/main/training_args.bin
1.33 kB
- Xet hash:
- 8b610d6c099834034efc2cbbd3803c1fb668bbe54100a8f63ee1e97f9fd38db4
- Size of remote file:
- 1.33 kB
- SHA256:
- 82303589f1c3b8a44b3ecd059c5a0e6478be4ab30a9d54d7d295afbbabe34ca6
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