Instructions to use BSC-LT/salamandra-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BSC-LT/salamandra-2b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BSC-LT/salamandra-2b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BSC-LT/salamandra-2b") model = AutoModelForCausalLM.from_pretrained("BSC-LT/salamandra-2b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use BSC-LT/salamandra-2b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "BSC-LT/salamandra-2b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "BSC-LT/salamandra-2b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/BSC-LT/salamandra-2b
- SGLang
How to use BSC-LT/salamandra-2b 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 "BSC-LT/salamandra-2b" \ --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": "BSC-LT/salamandra-2b", "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 "BSC-LT/salamandra-2b" \ --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": "BSC-LT/salamandra-2b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use BSC-LT/salamandra-2b with Docker Model Runner:
docker model run hf.co/BSC-LT/salamandra-2b
Add dataset contribution from CENID (#4)
Browse files- Add dataset contribution from CENID (af1f1203b2aa81fef95491d7f5e34692d5fe9af4)
Co-authored-by: José Javier Saiz <jsaizant@users.noreply.huggingface.co>
README.md
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| Bulgarian National Corpus (BulNC) | bg | [Link](http://old.dcl.bas.bg/dataset/BulNC.7z) |
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| CaBeRnet | fr | Popa-Fabre et al., 2020 |
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| CATalog 1.0 | ca | Palomar-Giner et al., 2024 |
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| CorpusNÓS | gl | de-Dios-Flores et al., 2024 |
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| Croatian Web as Corpus 2.1 (hrWaC) | hr | Ljubešić & Klubička, 2014 |
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| DaNewsroom | da | Varab & Schluter, 2020 |
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| Bulgarian National Corpus (BulNC) | bg | [Link](http://old.dcl.bas.bg/dataset/BulNC.7z) |
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| CaBeRnet | fr | Popa-Fabre et al., 2020 |
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| 346 |
| CATalog 1.0 | ca | Palomar-Giner et al., 2024 |
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| CATalog 1.0-va | ca | BOUA, DOGC, DOGV, Les Corts Valencianes, provided by CENID |
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| CorpusNÓS | gl | de-Dios-Flores et al., 2024 |
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| Croatian Web as Corpus 2.1 (hrWaC) | hr | Ljubešić & Klubička, 2014 |
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| DaNewsroom | da | Varab & Schluter, 2020 |
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