Instructions to use iboing/CorDA_IPA_math_finetuned_math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iboing/CorDA_IPA_math_finetuned_math with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="iboing/CorDA_IPA_math_finetuned_math", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("iboing/CorDA_IPA_math_finetuned_math", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("iboing/CorDA_IPA_math_finetuned_math", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use iboing/CorDA_IPA_math_finetuned_math with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "iboing/CorDA_IPA_math_finetuned_math" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "iboing/CorDA_IPA_math_finetuned_math", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/iboing/CorDA_IPA_math_finetuned_math
- SGLang
How to use iboing/CorDA_IPA_math_finetuned_math 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 "iboing/CorDA_IPA_math_finetuned_math" \ --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": "iboing/CorDA_IPA_math_finetuned_math", "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 "iboing/CorDA_IPA_math_finetuned_math" \ --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": "iboing/CorDA_IPA_math_finetuned_math", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use iboing/CorDA_IPA_math_finetuned_math with Docker Model Runner:
docker model run hf.co/iboing/CorDA_IPA_math_finetuned_math
metadata
license: llama2
The LLaMA-2-7b model finetuned on the Math task using CorDA in the IPA mode with MetaMath.
| Method | TriviaQA | NQ open | GSM8k | Math |
|---|---|---|---|---|
| LoRA | 44.17 | 1.91 | 42.68 | 5.92 |
| CorDA (KPA with nqopen) | 45.23 | 10.44 | 45.64 | 6.94 |
| CorDA (IPA with MetaMath) | - | - | 54.59 | 8.54 |
You can evaluate the model's performance following the step-3 in CorDA github repo.
Note: The model trained using CorDA adapter is based on customized code. If you want to restore the original LLaMA architecture, execute merge_adapter_for_corda.py in CorDA github repo.