Athena 3.1
Collection
Apache Labs' fine-tunes of the LlaMA 3.1 Family
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Athena 3.1 405B
Athena 3.1 405B is a custom fine-tune of Meta's LLaMA 3.1 405B model, designed for advanced text generation and conversational tasks. Fine-tuned by Apache Labs, this model enhances LLaMA's original capabilities with improved performance in specialized domains and nuanced text understanding.
Model Summary
- Base Model:
LLaMA 3.1 405B - Fine-Tuned Model Name: Athena 3.1 405B
- Purpose: General-purpose language generation, with fine-tuning for improved context comprehension and specialized domain handling.
Key Features
- Enhanced Comprehension: Fine-tuned for more accurate context retention over long conversations and complex queries.
- Specialized Knowledge: Trained with an additional dataset to improve performance in specific domains (e.g., technical support, scientific analysis).
- Scalability: Capable of handling large-scale text generation tasks across various applications.
Quickstart Guide
To get started with Athena 3.1 70B, you can use it in a Hugging Face environment with the following setup:
Use a pipeline as a high-level helper:
from transformers import pipeline
# Define the pipeline
pipe = pipeline("text-generation", model="apache-labs/Athena-3.1-405B")
# Define a prompt
messages = [
{"role": "user", "content": "Who are you?"},
]
# Generate response
response = pipe(messages)
print(response)
Load Model Directly:
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("apache-labs/Athena-3.1-405B")
model = AutoModelForCausalLM.from_pretrained("apache-labs/Athena-3.1-405B")
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Base model
meta-llama/Llama-3.1-405B