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AndrewThompson1233
/
maba-v1.5-exp-architecture

Text Generation
Transformers
PyTorch
English
maba_sparse
maba
maba-v1.5
maba-v1.5-exp
architecture
recurrent
decoupled-gated-delta-attention
dgda
linear-attention
linear-recurrence
sparse-attention
maba-sa
mla
multi-head-latent-attention
nope
dg-indexer
centroid-indexing
hca
3-stream
swiglu
rmsnorm
speculative-decoding
mtp
multi-token-prediction
scaling
100m
1b
3b
7b
30b
Model card Files Files and versions
xet
Community

Instructions to use AndrewThompson1233/maba-v1.5-exp-architecture with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use AndrewThompson1233/maba-v1.5-exp-architecture with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="AndrewThompson1233/maba-v1.5-exp-architecture")
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("AndrewThompson1233/maba-v1.5-exp-architecture", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use AndrewThompson1233/maba-v1.5-exp-architecture with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "AndrewThompson1233/maba-v1.5-exp-architecture"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "AndrewThompson1233/maba-v1.5-exp-architecture",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/AndrewThompson1233/maba-v1.5-exp-architecture
  • SGLang

    How to use AndrewThompson1233/maba-v1.5-exp-architecture 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 "AndrewThompson1233/maba-v1.5-exp-architecture" \
        --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": "AndrewThompson1233/maba-v1.5-exp-architecture",
    		"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 "AndrewThompson1233/maba-v1.5-exp-architecture" \
            --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": "AndrewThompson1233/maba-v1.5-exp-architecture",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use AndrewThompson1233/maba-v1.5-exp-architecture with Docker Model Runner:

    docker model run hf.co/AndrewThompson1233/maba-v1.5-exp-architecture
maba-v1.5-exp-architecture
354 kB
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  • 3 contributors
History: 28 commits
AndrewThompson1233's picture
AndrewThompson1233
commit28
f85f5ad 11 days ago
  • assets
    commit2 17 days ago
  • maba_sparse
    commit2 17 days ago
  • scripts
    commit13 17 days ago
  • tests
    commit17 17 days ago
  • .gitattributes
    1.52 kB
    commit1 17 days ago
  • .gitignore
    288 Bytes
    commit23 17 days ago
  • BENCHMARK_REPORT.md
    2 kB
    commit20 17 days ago
  • LICENSE
    1.64 kB
    commit24 15 days ago
  • MABA_SPARSE_SPEC_AND_ROADMAP.md
    30.4 kB
    commit2 17 days ago
  • README.md
    11.4 kB
    commit28 11 days ago
  • SCALING.md
    11 kB
    commit14 17 days ago
  • benchmark.py
    9.71 kB
    commit2 17 days ago
  • benchmark_results.json
    2.14 kB
    commit20 17 days ago
  • config.json
    832 Bytes
    commit16 17 days ago
  • large_scale_results.json
    1.48 kB
    commit2 17 days ago
  • pyproject.toml
    1.29 kB
    commit2 17 days ago
  • train.py
    6.7 kB
    commit19 17 days ago