Text Generation
Transformers
Safetensors
mixtral
Mixture of Experts
frankenmoe
Merge
mergekit
lazymergekit
yam-peleg/Experiment26-7B
mlabonne/AlphaMonarch-7B
Eval Results (legacy)
text-generation-inference
Instructions to use jsfs11/MixtureofMerges-MoE-2x7b-v6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jsfs11/MixtureofMerges-MoE-2x7b-v6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jsfs11/MixtureofMerges-MoE-2x7b-v6")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jsfs11/MixtureofMerges-MoE-2x7b-v6") model = AutoModelForCausalLM.from_pretrained("jsfs11/MixtureofMerges-MoE-2x7b-v6") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jsfs11/MixtureofMerges-MoE-2x7b-v6 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jsfs11/MixtureofMerges-MoE-2x7b-v6" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jsfs11/MixtureofMerges-MoE-2x7b-v6", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jsfs11/MixtureofMerges-MoE-2x7b-v6
- SGLang
How to use jsfs11/MixtureofMerges-MoE-2x7b-v6 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 "jsfs11/MixtureofMerges-MoE-2x7b-v6" \ --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": "jsfs11/MixtureofMerges-MoE-2x7b-v6", "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 "jsfs11/MixtureofMerges-MoE-2x7b-v6" \ --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": "jsfs11/MixtureofMerges-MoE-2x7b-v6", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jsfs11/MixtureofMerges-MoE-2x7b-v6 with Docker Model Runner:
docker model run hf.co/jsfs11/MixtureofMerges-MoE-2x7b-v6
Adding Evaluation Results
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by leaderboard-pr-bot - opened
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@@ -222,3 +222,17 @@ Detailed results can be found [here](https://huggingface.co/datasets/open-llm-le
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|Winogrande (5-shot) |84.77|
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|GSM8k (5-shot) |69.37|
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|Winogrande (5-shot) |84.77|
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|GSM8k (5-shot) |69.37|
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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_jsfs11__MixtureofMerges-MoE-2x7b-v6)
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| Metric |Value|
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|Avg. |76.63|
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|AI2 Reasoning Challenge (25-Shot)|73.38|
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|HellaSwag (10-Shot) |89.16|
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|MMLU (5-Shot) |64.53|
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|TruthfulQA (0-shot) |78.58|
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|Winogrande (5-shot) |84.77|
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|GSM8k (5-shot) |69.37|
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