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zapabobouj
/
AEGIS-v2.5-SO8T-Quadrality-imatrix

Text Generation
Transformers
Safetensors
GGUF
English
Japanese
so8-quadrality-inference
mathematical-reasoning
continual-learning
enhanced-moonshot-pipeline
industry-standard-benchmarks
elyza-tasks-100
deepseek-grpo
mhc-manifold
geometric-scaling
imatrix-quantization
statistical-significance
scientific-rigor
ablation-study
baseline-comparison
evaluation-standardization
abc-testing
multilingual
conversational
Model card Files Files and versions
xet
Community

Instructions to use zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix
  • SGLang

    How to use zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix 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 "zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix" \
        --host 0.0.0.0 \
        --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    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 "zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix" \
            --host 0.0.0.0 \
            --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix with Docker Model Runner:

    docker model run hf.co/zapabobouj/AEGIS-v2.5-SO8T-Quadrality-imatrix
AEGIS-v2.5-SO8T-Quadrality-imatrix / abc_test_charts
1.13 MB
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  • 1 contributor
History: 11 commits
zapabobouj's picture
zapabobouj
Upload ABC test analysis: create_abc_test_charts.py
1b5c969 verified 7 months ago
  • abc_benchmark_overview.png
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  • abc_performance_comparison.png
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  • abc_significance_visualization.png
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  • abc_test_report.md
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  • abc_test_results.json
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  • create_abc_test_charts.py
    15.3 kB
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