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abe123
/
babylm-dat-strict-nextlat-final

Text Generation
Transformers
Safetensors
dat
babylm
babylm-2026
causal-lm
dual-attention-transformer
nextlat
ema
custom-code
custom_code
Model card Files Files and versions
xet
Community

Instructions to use abe123/babylm-dat-strict-nextlat-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use abe123/babylm-dat-strict-nextlat-final with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="abe123/babylm-dat-strict-nextlat-final", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("abe123/babylm-dat-strict-nextlat-final", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use abe123/babylm-dat-strict-nextlat-final with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "abe123/babylm-dat-strict-nextlat-final"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "abe123/babylm-dat-strict-nextlat-final",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/abe123/babylm-dat-strict-nextlat-final
  • SGLang

    How to use abe123/babylm-dat-strict-nextlat-final 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 "abe123/babylm-dat-strict-nextlat-final" \
        --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": "abe123/babylm-dat-strict-nextlat-final",
    		"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 "abe123/babylm-dat-strict-nextlat-final" \
            --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": "abe123/babylm-dat-strict-nextlat-final",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use abe123/babylm-dat-strict-nextlat-final with Docker Model Runner:

    docker model run hf.co/abe123/babylm-dat-strict-nextlat-final
babylm-dat-strict-nextlat-final
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  • 1 contributor
History: 2 commits
abe123's picture
abe123
Publish final EMA checkpoint
2719787 verified 9 days ago
  • .gitattributes
    1.52 kB
    initial commit 9 days ago
  • README.md
    2.61 kB
    Publish final EMA checkpoint 9 days ago
  • attention_config.py
    150 Bytes
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  • config.json
    1.96 kB
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  • configuration_dat.py
    7.77 kB
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  • dat_config.py
    13.7 kB
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  • dat_core.py
    12.5 kB
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  • dat_lm.py
    19.9 kB
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  • dat_symbols.py
    10.7 kB
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  • masks.py
    2.34 kB
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  • mlm.py
    3.34 kB
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  • model.safetensors
    1.19 GB
    xet
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  • modeling_dat.py
    12.1 kB
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  • special_tokens_map.json
    150 Bytes
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  • tokenizer.json
    1.67 MB
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  • tokenizer_config.json
    230 Bytes
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  • transformer_components.py
    11.7 kB
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  • transformer_core.py
    13.6 kB
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