Text Generation
Transformers
Safetensors
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Eval Results (legacy)
text-generation-inference
Instructions to use altomek/Midnight-Rose-70B-v2.0.3-3.75bpw-EXL2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use altomek/Midnight-Rose-70B-v2.0.3-3.75bpw-EXL2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="altomek/Midnight-Rose-70B-v2.0.3-3.75bpw-EXL2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("altomek/Midnight-Rose-70B-v2.0.3-3.75bpw-EXL2") model = AutoModelForCausalLM.from_pretrained("altomek/Midnight-Rose-70B-v2.0.3-3.75bpw-EXL2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use altomek/Midnight-Rose-70B-v2.0.3-3.75bpw-EXL2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "altomek/Midnight-Rose-70B-v2.0.3-3.75bpw-EXL2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "altomek/Midnight-Rose-70B-v2.0.3-3.75bpw-EXL2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/altomek/Midnight-Rose-70B-v2.0.3-3.75bpw-EXL2
- SGLang
How to use altomek/Midnight-Rose-70B-v2.0.3-3.75bpw-EXL2 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 "altomek/Midnight-Rose-70B-v2.0.3-3.75bpw-EXL2" \ --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": "altomek/Midnight-Rose-70B-v2.0.3-3.75bpw-EXL2", "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 "altomek/Midnight-Rose-70B-v2.0.3-3.75bpw-EXL2" \ --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": "altomek/Midnight-Rose-70B-v2.0.3-3.75bpw-EXL2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use altomek/Midnight-Rose-70B-v2.0.3-3.75bpw-EXL2 with Docker Model Runner:
docker model run hf.co/altomek/Midnight-Rose-70B-v2.0.3-3.75bpw-EXL2
Midnight-Rose-70B-v2.0.3
ExLlamav2 3.75 bpw quants of https://huggingface.co/sophosympatheia/Midnight-Rose-70B-v2.0.3
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Model tree for altomek/Midnight-Rose-70B-v2.0.3-3.75bpw-EXL2
Base model
sophosympatheia/Midnight-Rose-70B-v2.0.3Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard70.650
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard87.500
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard69.640
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard65.270
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard81.220
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard28.350