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

DinoResearch
/
EspBPE-49K

Text Generation
Transformers
Spanish
spanish
tokenizer
49512
vocabsize
español
espanhol
espanol
tokenizacion
dinoresearch
dino
rextro111
REXTRO111
RexTRO111
rextro
Model card Files Files and versions
xet
Community

Instructions to use DinoResearch/EspBPE-49K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use DinoResearch/EspBPE-49K with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="DinoResearch/EspBPE-49K")
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("DinoResearch/EspBPE-49K", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use DinoResearch/EspBPE-49K with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "DinoResearch/EspBPE-49K"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "DinoResearch/EspBPE-49K",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/DinoResearch/EspBPE-49K
  • SGLang

    How to use DinoResearch/EspBPE-49K 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 "DinoResearch/EspBPE-49K" \
        --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": "DinoResearch/EspBPE-49K",
    		"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 "DinoResearch/EspBPE-49K" \
            --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": "DinoResearch/EspBPE-49K",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use DinoResearch/EspBPE-49K with Docker Model Runner:

    docker model run hf.co/DinoResearch/EspBPE-49K
EspBPE-49K
3.55 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 4 commits
RexTRO111's picture
RexTRO111
Update README.md
443a9a9 verified 5 days ago
  • .gitattributes
    1.52 kB
    initial commit 5 days ago
  • README.md
    4.89 kB
    Update README.md 5 days ago
  • tokenizer.json
    3.54 MB
    Upload custom 49,152 vocab Spanish BPE tokenizer 5 days ago
  • tokenizer_config.json
    295 Bytes
    Upload custom 49,152 vocab Spanish BPE tokenizer 5 days ago