Instructions to use quantumaikr/QuantumLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use quantumaikr/QuantumLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="quantumaikr/QuantumLM")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("quantumaikr/QuantumLM") model = AutoModelForCausalLM.from_pretrained("quantumaikr/QuantumLM") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use quantumaikr/QuantumLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "quantumaikr/QuantumLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "quantumaikr/QuantumLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/quantumaikr/QuantumLM
- SGLang
How to use quantumaikr/QuantumLM 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 "quantumaikr/QuantumLM" \ --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": "quantumaikr/QuantumLM", "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 "quantumaikr/QuantumLM" \ --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": "quantumaikr/QuantumLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use quantumaikr/QuantumLM with Docker Model Runner:
docker model run hf.co/quantumaikr/QuantumLM
Adding Evaluation Results
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README.md
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Although the aforementioned dataset helps to steer the base language models into "safer" distributions of text, not all biases and toxicity can be mitigated through fine-tuning. We ask that users be mindful of such potential issues that can arise in generated responses. Do not treat model outputs as substitutes for human judgment or as sources of truth. Please use it responsibly.
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Although the aforementioned dataset helps to steer the base language models into "safer" distributions of text, not all biases and toxicity can be mitigated through fine-tuning. We ask that users be mindful of such potential issues that can arise in generated responses. Do not treat model outputs as substitutes for human judgment or as sources of truth. Please use it responsibly.
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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_quantumaikr__QuantumLM)
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 46.73 |
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| ARC (25-shot) | 55.8 |
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| HellaSwag (10-shot) | 79.74 |
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| MMLU (5-shot) | 54.17 |
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| TruthfulQA (0-shot) | 46.71 |
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| Winogrande (5-shot) | 74.19 |
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| GSM8K (5-shot) | 9.86 |
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| DROP (3-shot) | 6.65 |
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