Instructions to use Dimensity/Dimensity-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dimensity/Dimensity-3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Dimensity/Dimensity-3B", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Dimensity/Dimensity-3B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Dimensity/Dimensity-3B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Dimensity/Dimensity-3B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Dimensity/Dimensity-3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Dimensity/Dimensity-3B
- SGLang
How to use Dimensity/Dimensity-3B 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 "Dimensity/Dimensity-3B" \ --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": "Dimensity/Dimensity-3B", "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 "Dimensity/Dimensity-3B" \ --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": "Dimensity/Dimensity-3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Dimensity/Dimensity-3B with Docker Model Runner:
docker model run hf.co/Dimensity/Dimensity-3B
| license: mit | |
| language: | |
| - en | |
| tags: | |
| - sft | |
| ```Dimensity-3B``` | |
| # Model Details | |
| Dimensity-3B is a finetuned version of the StableLM framework trained on a variety of conversational data. It contains 3 billion parameters. | |
| # Intended Uses | |
| This model is intended for conversational AI applications. It can engage in open-ended dialogue by generating responses to user prompts. | |
| ## Factors | |
| # Training Data | |
| The model was trained on a large dataset of over 100 million conversational exchanges extracted from Reddit comments, customer support logs, and other online dialogues. | |
| # Prompt Template | |
| The model was finetuned using the following prompt template: | |
| ``` | |
| ### Human: {prompt} | |
| ### Assistant: | |
| ``` | |
| This prompts the model to take on an assistant role. | |
| # Ethical Considerations | |
| As the model was trained on public conversational data, it may generate responses that contain harmful stereotypes or toxic content. The model should be used with caution in sensitive contexts. | |
| # Caveats and Recommendations | |
| This model is designed for open-ended conversation. It may sometimes generate plausible-sounding but incorrect information. Outputs should be validated against external sources. |