Feature Extraction
Transformers
Safetensors
German
llama
masked-lm
long-context
text-embeddings-inference
Instructions to use LSX-UniWue/LLaMmlein2Vec_7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LSX-UniWue/LLaMmlein2Vec_7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LSX-UniWue/LLaMmlein2Vec_7B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LSX-UniWue/LLaMmlein2Vec_7B") model = AutoModel.from_pretrained("LSX-UniWue/LLaMmlein2Vec_7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from LSX-UniWue/LLaMmlein2Vec_7B: direct link, hf CLI and curl.
- Browser
- Download file 793 kB
-
https://huggingface.co/LSX-UniWue/LLaMmlein2Vec_7B/resolve/main/tokenizer.model
- Command line
-
hf download hf://LSX-UniWue/LLaMmlein2Vec_7B/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/LSX-UniWue/LLaMmlein2Vec_7B/resolve/main/tokenizer.model
793 kB
- Xet hash:
- 3ed5e917faca47b23b8c1aae71a851f7fb6b49ef7cfa2073400ebacccaeffbb9
- Size of remote file:
- 793 kB
- SHA256:
- 5377b19312a04a9415012261bcd8aa0b6ac199ec3a5d5d3d53f506c0e69c5d6d
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