Instructions to use camel-ai/CAMEL-13B-Combined-Data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use camel-ai/CAMEL-13B-Combined-Data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="camel-ai/CAMEL-13B-Combined-Data")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("camel-ai/CAMEL-13B-Combined-Data") model = AutoModelForCausalLM.from_pretrained("camel-ai/CAMEL-13B-Combined-Data", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use camel-ai/CAMEL-13B-Combined-Data with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "camel-ai/CAMEL-13B-Combined-Data" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "camel-ai/CAMEL-13B-Combined-Data", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/camel-ai/CAMEL-13B-Combined-Data
- SGLang
How to use camel-ai/CAMEL-13B-Combined-Data 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 "camel-ai/CAMEL-13B-Combined-Data" \ --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": "camel-ai/CAMEL-13B-Combined-Data", "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 "camel-ai/CAMEL-13B-Combined-Data" \ --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": "camel-ai/CAMEL-13B-Combined-Data", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use camel-ai/CAMEL-13B-Combined-Data with Docker Model Runner:
docker model run hf.co/camel-ai/CAMEL-13B-Combined-Data
Duplicate files?
Dear CAMEL Team, I have finished reading your paper and I am excited to test both models. However, I noticed that the model files uploaded for CAMEL-13B-Role-Playing-Data and CAMEL-13B-Combined-Data appear to be duplicates. For example, the file pytorch_model-00001-of-00006.bin has the same hash in both models (SHA256: 08d6c3bdd4c92abafdbc2e6a69d73fb9b4891cfdcd770e80bfed13673232fd04). Could you please look into this and let me know which model was actually uploaded?
Hello @MachineThinker ,
We re-uploaded the models and made sure the hashes are distinct. Let us know if you're still having the same problem.
Oh wow! Thanks for the heads up. Huh, it's amazing it still produced coherent responses!
Did both files change? Do I need to re-do both?
Yeah I guess I do as all files changed in both repos
@TheBloke You can requantize our models but they're obsolete now. We're working on better models. We'll release them soon. Thanks for your time and effort!
OK good to know! I just started them, may as well have them working.
But I'll look forward to the new models and quantise those as soon as they're out!
Thanks very much for the great model releases.