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Intel
/
fid_flan_t5_base_nq

Text Generation
Transformers
PyTorch
Safetensors
English
t5
Eval Results (legacy)
text-generation-inference
Model card Files Files and versions
xet
Community
1

Instructions to use Intel/fid_flan_t5_base_nq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Intel/fid_flan_t5_base_nq with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Intel/fid_flan_t5_base_nq")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
    
    tokenizer = AutoTokenizer.from_pretrained("Intel/fid_flan_t5_base_nq")
    model = AutoModelForSeq2SeqLM.from_pretrained("Intel/fid_flan_t5_base_nq")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use Intel/fid_flan_t5_base_nq with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Intel/fid_flan_t5_base_nq"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Intel/fid_flan_t5_base_nq",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/Intel/fid_flan_t5_base_nq
  • SGLang

    How to use Intel/fid_flan_t5_base_nq 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 "Intel/fid_flan_t5_base_nq" \
        --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": "Intel/fid_flan_t5_base_nq",
    		"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 "Intel/fid_flan_t5_base_nq" \
            --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": "Intel/fid_flan_t5_base_nq",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Intel/fid_flan_t5_base_nq with Docker Model Runner:

    docker model run hf.co/Intel/fid_flan_t5_base_nq
fid_flan_t5_base_nq
994 MB
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  • 3 contributors
History: 11 commits
mber's picture
mber
Upload 4 files
f282568 over 2 years ago
  • .gitattributes
    1.48 kB
    initial commit about 3 years ago
  • README.md
    1.08 kB
    Update README.md about 3 years ago
  • config.json
    1.54 kB
    Upload 2 files about 3 years ago
  • model.safetensors
    495 MB
    xet
    Adding `safetensors` variant of this model (#1) over 2 years ago
  • pytorch_model.bin

    Detected Pickle imports (3)

    • "torch.BFloat16Storage",
    • "collections.OrderedDict",
    • "torch._utils._rebuild_tensor_v2"

    What is a pickle import?

    495 MB
    xet
    Upload 2 files about 3 years ago
  • special_tokens_map.json
    2.2 kB
    Upload 4 files over 2 years ago
  • spiece.model
    792 kB
    xet
    Upload 4 files over 2 years ago
  • tokenizer.json
    2.42 MB
    Upload 4 files over 2 years ago
  • tokenizer_config.json
    2.35 kB
    Upload 4 files over 2 years ago