Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

AndrewThompson1233
/
maba-v2-architecture

Text Generation
Transformers
RWKV
PyTorch
English
maba_sparse
maba
maba-v2
maba-v2-architecture
architecture
recurrent
dgda
decoupled-gated-delta-attention
gated-deltanet
linear-attention
linear-recurrence
sparse-attention
maba-sa
mla
multi-head-latent-attention
deepseek
qwen
minicpm
mamba
mamba-2
transformer
causal-lm
llm
nlp
long-context
1m-context
sub-quadratic
state-space-model
ssm
triton
flash-attention
on-device-ai
efficient-llm
nope
dg-indexer
centroid-indexing
hca
3-stream
swiglu
rmsnorm
speculative-decoding
mtp
multi-token-prediction
needle-in-a-haystack
scaling
100m
1b
3b
7b
30b
Model card Files Files and versions
xet
Community

Instructions to use AndrewThompson1233/maba-v2-architecture with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use AndrewThompson1233/maba-v2-architecture with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="AndrewThompson1233/maba-v2-architecture")
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("AndrewThompson1233/maba-v2-architecture", device_map="auto")
  • RWKV

    How to use AndrewThompson1233/maba-v2-architecture with RWKV:

    # No code snippets available yet for this library.
    
    # To use this model, check the repository files and the library's documentation.
    
    # Want to help? PRs adding snippets are welcome at:
    # https://github.com/huggingface/huggingface.js
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use AndrewThompson1233/maba-v2-architecture with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "AndrewThompson1233/maba-v2-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-v2-architecture",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/AndrewThompson1233/maba-v2-architecture
  • SGLang

    How to use AndrewThompson1233/maba-v2-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-v2-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-v2-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-v2-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-v2-architecture",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use AndrewThompson1233/maba-v2-architecture with Docker Model Runner:

    docker model run hf.co/AndrewThompson1233/maba-v2-architecture
maba-v2-architecture
665 kB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 39 commits
AndrewThompson1233's picture
AndrewThompson1233
commit39
504cd82 1 day ago
  • assets
    commit31 14 days ago
  • maba_sparse
    commit26 14 days ago
  • tests
    commit27 14 days ago
  • .gitattributes
    1.52 kB
    commit1 20 days ago
  • .gitignore
    552 Bytes
    commit27 14 days ago
  • BENCHMARK_REPORT.md
    7.94 kB
    commit39 1 day ago
  • LICENSE
    4.06 kB
    commit30 14 days ago
  • README.md
    7.36 kB
    commit39 1 day ago
  • SCALING.md
    6.94 kB
    commit37 13 days ago
  • benchmark.py
    33.6 kB
    commit39 1 day ago
  • benchmark_results.json
    5.43 kB
    commit26 14 days ago
  • config.json
    832 Bytes
    commit26 14 days ago
  • pyproject.toml
    1.51 kB
    commit35 14 days ago
  • train.py
    10.3 kB
    commit26 14 days ago