Instructions to use Undi95/ReMM-v2-L2-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Undi95/ReMM-v2-L2-13B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Undi95/ReMM-v2-L2-13B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Undi95/ReMM-v2-L2-13B") model = AutoModelForCausalLM.from_pretrained("Undi95/ReMM-v2-L2-13B") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Undi95/ReMM-v2-L2-13B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Undi95/ReMM-v2-L2-13B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Undi95/ReMM-v2-L2-13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Undi95/ReMM-v2-L2-13B
- SGLang
How to use Undi95/ReMM-v2-L2-13B 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 "Undi95/ReMM-v2-L2-13B" \ --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": "Undi95/ReMM-v2-L2-13B", "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 "Undi95/ReMM-v2-L2-13B" \ --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": "Undi95/ReMM-v2-L2-13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Undi95/ReMM-v2-L2-13B with Docker Model Runner:
docker model run hf.co/Undi95/ReMM-v2-L2-13B
Adding Evaluation Results
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Special thanks to Sushi kek
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Special thanks to Sushi kek
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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_Undi95__ReMM-v2-L2-13B)
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| Metric | Value |
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| Avg. | 50.57 |
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| ARC (25-shot) | 61.95 |
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| HellaSwag (10-shot) | 84.0 |
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| MMLU (5-shot) | 56.14 |
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| TruthfulQA (0-shot) | 50.81 |
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| Winogrande (5-shot) | 75.85 |
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| GSM8K (5-shot) | 13.19 |
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| DROP (3-shot) | 12.08 |
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