Instructions to use raaec/Meta-Llama-3.1-8B-Instruct-Summarizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raaec/Meta-Llama-3.1-8B-Instruct-Summarizer with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="raaec/Meta-Llama-3.1-8B-Instruct-Summarizer")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("raaec/Meta-Llama-3.1-8B-Instruct-Summarizer") model = AutoModelForCausalLM.from_pretrained("raaec/Meta-Llama-3.1-8B-Instruct-Summarizer") - Notebooks
- Google Colab
- Kaggle
Model Information
This is a fine-tuned version of Llama 3.1 trained in English, Spanish, and Chinese for text summarization.
The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
Model developer: Meta
Model Architecture: Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
- Downloads last month
- 10