Instructions to use HyperbeeAI/Tulpar-7b-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HyperbeeAI/Tulpar-7b-v0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="HyperbeeAI/Tulpar-7b-v0")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("HyperbeeAI/Tulpar-7b-v0") model = AutoModelForCausalLM.from_pretrained("HyperbeeAI/Tulpar-7b-v0") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use HyperbeeAI/Tulpar-7b-v0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "HyperbeeAI/Tulpar-7b-v0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "HyperbeeAI/Tulpar-7b-v0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/HyperbeeAI/Tulpar-7b-v0
- SGLang
How to use HyperbeeAI/Tulpar-7b-v0 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 "HyperbeeAI/Tulpar-7b-v0" \ --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": "HyperbeeAI/Tulpar-7b-v0", "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 "HyperbeeAI/Tulpar-7b-v0" \ --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": "HyperbeeAI/Tulpar-7b-v0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use HyperbeeAI/Tulpar-7b-v0 with Docker Model Runner:
docker model run hf.co/HyperbeeAI/Tulpar-7b-v0
Adding Evaluation Results
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|**Average**| |**0.3754**
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# Ethical Considerations and Limitations
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Tulpar is a technology with potential risks and limitations. This model is finetuned only in English and all language-related scenarios are not covered. As HyperbeeAI, we neither guarantee ethical, accurate, unbiased, objective responses nor endorse its outputs. Before deploying this model, you are advised to make safety tests for your use case.
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|**Average**| |**0.3754**
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# Ethical Considerations and Limitations
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Tulpar is a technology with potential risks and limitations. This model is finetuned only in English and all language-related scenarios are not covered. As HyperbeeAI, we neither guarantee ethical, accurate, unbiased, objective responses nor endorse its outputs. Before deploying this model, you are advised to make safety tests for your use case.
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_HyperbeeAI__Tulpar-7b-v0)
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| Metric | Value |
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| Avg. | 50.84 |
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| ARC (25-shot) | 56.31 |
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| HellaSwag (10-shot) | 79.01 |
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| MMLU (5-shot) | 52.55 |
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| TruthfulQA (0-shot) | 51.68 |
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| Winogrande (5-shot) | 73.88 |
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| GSM8K (5-shot) | 2.73 |
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| DROP (3-shot) | 39.75 |
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