Instructions to use MayaPH/GodziLLa2-70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MayaPH/GodziLLa2-70B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MayaPH/GodziLLa2-70B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MayaPH/GodziLLa2-70B") model = AutoModelForCausalLM.from_pretrained("MayaPH/GodziLLa2-70B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MayaPH/GodziLLa2-70B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MayaPH/GodziLLa2-70B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MayaPH/GodziLLa2-70B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MayaPH/GodziLLa2-70B
- SGLang
How to use MayaPH/GodziLLa2-70B 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 "MayaPH/GodziLLa2-70B" \ --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": "MayaPH/GodziLLa2-70B", "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 "MayaPH/GodziLLa2-70B" \ --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": "MayaPH/GodziLLa2-70B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MayaPH/GodziLLa2-70B with Docker Model Runner:
docker model run hf.co/MayaPH/GodziLLa2-70B
Update README.md
Browse files
README.md
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@@ -27,7 +27,10 @@ GodziLLa 2 70B is an experimental combination of various proprietary LoRAs from
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| Winogrande (5-shot) | 83.19 |
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| GSM8K (5-shot) | 43.21 |
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| DROP (3-shot) | 52.31 |
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According to the leaderboard description, here are the benchmarks used for the evaluation:
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- [MMLU](https://arxiv.org/abs/2009.03300) (5-shot) - a test to measure a text model’s multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more.
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| Winogrande (5-shot) | 83.19 |
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| GSM8K (5-shot) | 43.21 |
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| DROP (3-shot) | 52.31 |
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| Average (w/ DROP) | 67.01 |
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| Average (w/o DROP) | 69.46 |
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Note: As of December 1, 2023, [DROP](https://arxiv.org/abs/1903.00161) is removed from the leaderboard benchmarks.
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According to the leaderboard description, here are the benchmarks used for the evaluation:
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- [MMLU](https://arxiv.org/abs/2009.03300) (5-shot) - a test to measure a text model’s multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more.
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