Instructions to use Adrienfndr/luna-stage1-backbone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use Adrienfndr/luna-stage1-backbone with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("<base-model-id>") model.load_adapter("Adrienfndr/luna-stage1-backbone", set_active=True) - Notebooks
- Google Colab
- Kaggle
Luna Stage 1 Backbone
This is a fine-tuned version of Llama-2-7B-chat, trained on conversational data for improved dialogue capabilities.
Model Details
- Base Model: meta-llama/Llama-2-7b-chat-hf
- Training Data: DailyDialog dataset (7,000 samples)
- Fine-tuning: Stage 1 backbone training
- Parameters: 6.7B parameters
Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Adrienfndr/luna-stage1-backbone")
model = AutoModelForCausalLM.from_pretrained("Adrienfndr/luna-stage1-backbone")
# Generate text
inputs = tokenizer("Hello, how are you?", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
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Model tree for Adrienfndr/luna-stage1-backbone
Base model
meta-llama/Llama-2-7b-chat-hf