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Model Description

Llama 3.1 70B is a large language model developed by Meta Research, trained on a diverse corpus of text data. This model excels at instruction following, conversation, and various natural language processing tasks.

Key Features

70 billion parameters for high-quality text generation
Optimized for instruction following and conversational AI
Support for multiple languages and coding tasks
Efficient inference with quantization support

Usage Example

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("meta-research/llama-3.1-70b")
tokenizer = AutoTokenizer.from_pretrained("meta-research/llama-3.1-70b")

prompt = "Explain quantum computing in simple terms:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0]))

Limitations

Like all large language models, this model may produce biased or incorrect outputs. Users should validate generated content and not use it for critical decision-making without human oversight.

Key Features

70 billion parameters for high-quality text generation
Optimized for instruction following and conversational AI
Support for multiple languages and coding tasks
Efficient inference with quantization support

Usage Example

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("meta-research/llama-3.1-70b")
tokenizer = AutoTokenizer.from_pretrained("meta-research/llama-3.1-70b")

prompt = "Explain quantum computing in simple terms:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0]))

Limitations

Like all large language models, this model may produce biased or incorrect outputs. Users should validate generated content and not use it for critical decision-making without human oversight.