Instructions to use aiplanet/effi-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aiplanet/effi-13b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="aiplanet/effi-13b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("aiplanet/effi-13b") model = AutoModelForCausalLM.from_pretrained("aiplanet/effi-13b") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use aiplanet/effi-13b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aiplanet/effi-13b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aiplanet/effi-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/aiplanet/effi-13b
- SGLang
How to use aiplanet/effi-13b 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 "aiplanet/effi-13b" \ --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": "aiplanet/effi-13b", "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 "aiplanet/effi-13b" \ --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": "aiplanet/effi-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use aiplanet/effi-13b with Docker Model Runner:
docker model run hf.co/aiplanet/effi-13b
Update README.md
Browse files✨ **effi-13b**
effi-13b is a 13B parameters causal decoder-only model built by AiPlanet based on Llama-2-13b-chat-hf and finetuned on CoT dataset is made available under the Apache 2.0 license.
✨ **Why use effi-13b?**
- Looking for a ready to use chat/instruct model that can provide rationale for a context provided .It is based on FLlama-2-13b-chat-hf .
💬 This is an instruct model, which may not be ideal for further finetuning. If you are interested in building your own instruct/chat model, we recommend starting from Llama-2-13b-chat-hf.
💬 You will need at least 85-100GB of memory to swiftly run inference .
✨ **Uses**
**Direct Use**
effi-13b has been finetuned on a Chain of Thought dataset.
**Out-of-Scope Use**
Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
**Bias, Risks, and Limitations**
effi-13bis mostly trained on English data, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
**Recommendations**
We recommend users of effi-13b to develop guardrails and to take appropriate precautions for any production use.