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LoNebula
/
Nebula-1-ablation-a2

Text Generation
Transformers
Safetensors
Japanese
English
llama
nebula
from-scratch
japanese
english
small-language-model
text-generation-inference
Model card Files Files and versions
xet
Community

Instructions to use LoNebula/Nebula-1-ablation-a2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use LoNebula/Nebula-1-ablation-a2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="LoNebula/Nebula-1-ablation-a2")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("LoNebula/Nebula-1-ablation-a2")
    model = AutoModelForCausalLM.from_pretrained("LoNebula/Nebula-1-ablation-a2", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use LoNebula/Nebula-1-ablation-a2 with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "LoNebula/Nebula-1-ablation-a2"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "LoNebula/Nebula-1-ablation-a2",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/LoNebula/Nebula-1-ablation-a2
  • SGLang

    How to use LoNebula/Nebula-1-ablation-a2 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 "LoNebula/Nebula-1-ablation-a2" \
        --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": "LoNebula/Nebula-1-ablation-a2",
    		"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 "LoNebula/Nebula-1-ablation-a2" \
            --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": "LoNebula/Nebula-1-ablation-a2",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use LoNebula/Nebula-1-ablation-a2 with Docker Model Runner:

    docker model run hf.co/LoNebula/Nebula-1-ablation-a2
Nebula-1-ablation-a2 / eval
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  • 1 contributor
History: 1 commit
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LoNebula
Upload Nebula-1-ablation-a2 (arm a2)
3f5249c verified 1 day ago
  • ablation-result.json
    2.18 kB
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  • arc_challenge.json
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  • arc_easy.json
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  • belebele_jpn_Jpan.json
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  • ja_leaderboard_xwinograd.json
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  • lambada_openai.json
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  • mmlu.json
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  • nebula_jcommonsenseqa.json
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  • nebula_jnli.json
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  • piqa.json
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  • val_metrics.json
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  • winogrande.json
    372 Bytes
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