Instructions to use rovai/chatbotmedium3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rovai/chatbotmedium3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rovai/chatbotmedium3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("rovai/chatbotmedium3") model = AutoModelForCausalLM.from_pretrained("rovai/chatbotmedium3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use rovai/chatbotmedium3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rovai/chatbotmedium3" # 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/chatbotmedium3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/rovai/chatbotmedium3
- SGLang
How to use rovai/chatbotmedium3 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/chatbotmedium3" \ --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/chatbotmedium3", "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/chatbotmedium3" \ --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/chatbotmedium3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use rovai/chatbotmedium3 with Docker Model Runner:
docker model run hf.co/rovai/chatbotmedium3
Commit History
-
Update README.md
2831d18 -
Update README.md
046a8d3 -
Create README.md
be9c494 -
Upload vocab.json
a5a77a4 -
Upload training_args.bin with git-lfs
2378fb3 -
Upload tokenizer.json
1e37b98 -
Upload tokenizer_config.json
459271b -
Upload special_tokens_map.json
e4629da -
Upload merges.txt
10a42d1 -
Upload eval_results.txt
ca159c1 -
Upload config.json
37f63d5 -
Upload pytorch_model.bin with git-lfs
3c53b38 -
initial commit
609e68c