Question Answering
Transformers
Safetensors
English
French
German
llama
text-generation
finance
economics
business
financial-analysis
economic-modeling
business-intelligence
text-generation-inference
Instructions to use OVHaiLLM/Llama-Open-Finance-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OVHaiLLM/Llama-Open-Finance-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="OVHaiLLM/Llama-Open-Finance-8B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("OVHaiLLM/Llama-Open-Finance-8B") model = AutoModelForCausalLM.from_pretrained("OVHaiLLM/Llama-Open-Finance-8B") - Notebooks
- Google Colab
- Kaggle
TemporalMesh Transformer: 29.4 PPL at 48% compute — beats Mamba, new open-source architecture
#1 opened about 1 month ago
by
vigneshwar234