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tangledgroup
/
tangled-alpha-0.9-core

Text Generation
Transformers
Safetensors
chat
core
base
instruct
reason
Model card Files Files and versions
xet
Community

Instructions to use tangledgroup/tangled-alpha-0.9-core with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use tangledgroup/tangled-alpha-0.9-core with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="tangledgroup/tangled-alpha-0.9-core")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("tangledgroup/tangled-alpha-0.9-core", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use tangledgroup/tangled-alpha-0.9-core with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "tangledgroup/tangled-alpha-0.9-core"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "tangledgroup/tangled-alpha-0.9-core",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/tangledgroup/tangled-alpha-0.9-core
  • SGLang

    How to use tangledgroup/tangled-alpha-0.9-core 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 "tangledgroup/tangled-alpha-0.9-core" \
        --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": "tangledgroup/tangled-alpha-0.9-core",
    		"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 "tangledgroup/tangled-alpha-0.9-core" \
            --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": "tangledgroup/tangled-alpha-0.9-core",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use tangledgroup/tangled-alpha-0.9-core with Docker Model Runner:

    docker model run hf.co/tangledgroup/tangled-alpha-0.9-core
tangled-alpha-0.9-core / scripts
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  • 2 contributors
History: 73 commits
mtasic85's picture
mtasic85
cpt core 4
7eb93cf about 1 year ago
  • backup
    cpt core 4 about 1 year ago
  • convert_pth_to_safetensors.py
    281 Bytes
    cpt core 4 about 1 year ago
  • core_base_datasets.py
    3.81 kB
    prepare datasets about 1 year ago
  • core_instruct_datasets.py
    2.13 kB
    prepare datasets about 1 year ago
  • cpt_core_model_4.py
    3.54 kB
    cpt core 4 about 1 year ago
  • prepare_core_datasets.py
    2.32 kB
    prepare datasets about 1 year ago
  • pretrain_core_model_0.yaml
    5.03 kB
    pretrain 1 about 1 year ago
  • pretrain_core_model_1.yaml
    5.05 kB
    pretrain 1 about 1 year ago
  • pretrain_core_model_2.yaml
    5.05 kB
    pretrain core 3 about 1 year ago
  • pretrain_core_model_3.yaml
    5.05 kB
    pretrain core 3 about 1 year ago
  • requirements-litgpt.in
    368 Bytes
    cpt core 4 about 1 year ago
  • requirements-unsloth.in
    112 Bytes
    cpt core 4 about 1 year ago
  • train_tokenizer.py
    2.62 kB
    prepare datasets about 1 year ago
  • utils.py
    5.34 kB
    prepare datasets about 1 year ago