Llama-3.2-3B-Instruct
The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out).
Code Completion (Llama 3.2-3B)
Code interpreter has emerged as one of the successful use cases for LLMs. In this example, we can learn how the models use the instructions to generate the code.
Input
Prompt* String
*
number
(minimum: 0, maximum: 1)
Default: 0.7
*
number
(minimum: 0, maximum: 1)
Default: 0.7
*
integer
(maximum: 1)
Default: 0.7
Output
Code interpreter output (Llama 3.2-3B)
Response
```python
def fibonacci(n):
"""
Calculate the n-th Fibonacci number.
Args:
n (int): The position of the Fibonacci number to calculate.
Returns:
int: The n-th Fibonacci number.
Raises:
ValueError: If n is a negative integer.
"""
if n < 0:
raise ValueError("n must be a non-negative integer")
elif n <= 1:
return n
else:
a, b =
Explainability: (DL Backtrace)
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The Llama 3.2-3B model is part of Meta's Llama 3.2 series, which features advanced large language models (LLMs) optimized for a variety of natural language processing tasks. With approximately 3.21 billion parameters, this model is designed to handle multilingual dialogue and excels in tasks such as text generation, summarization, and agentic retrieval. It represents a significant improvement over its predecessors, offering enhanced performance across multiple industry benchmarks.Model ArchitectureLlama 3.2 employs an auto-regressive transformer architecture, which is a common framework for modern language models. This architecture allows the model to generate text by predicting the next token in a sequence based on the preceding context. The 3B variant is characterized by:
- Parameter Count: Approximately 3.21 billion parameters, striking a balance between performance and resource efficiency.
- Context Length: Supports a context length of up to 128k tokens, enabling it to process long sequences of text effectively.
- Tokenization: Utilizes an improved tokenizer with a vocabulary size of 128K tokens, enhancing token efficiency and reducing the number of tokens processed compared to previous models.
Training Methodology
The training process for Llama 3.2-3B incorporates several advanced techniques:
- Pretraining: The model was pretrained on up to 9 trillion tokens from diverse publicly available datasets, allowing it to learn a wide range of linguistic patterns and knowledge.
- Instruction Tuning: The model undergoes supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to better align its outputs with user expectations for helpfulness and safety.
- Knowledge Distillation: Outputs from larger models (such as Llama 3.1's 8B and 70B variants) were used as targets during the pretraining phase, enhancing the learning process through knowledge distillation techniques.
Key Features
- Multilingual Capabilities: Llama 3.2-3B supports multiple languages, making it suitable for global applications in various linguistic contexts.
- High Performance: The model has demonstrated superior performance on benchmarks like MMLU (Massive Multitask Language Understanding), outperforming many existing open-source and closed chat models.
- Enhanced Instruction Following: With improvements in alignment and response diversity, the model exhibits better instruction-following capabilities compared to earlier versions.
- Grouped Query Attention (GQA): This technique has been implemented to improve inference efficiency, allowing the model to process queries more effectively.
Use Cases
Llama 3.2-3B is designed for a variety of applications:
- Text Generation: Capable of producing coherent and contextually relevant text based on user prompts.
- Summarization: Efficiently condenses information from longer texts into concise summaries.
- Conversational Agents: Facilitates the development of chatbots that can engage in meaningful dialogues across different languages.
Conclusion
The Llama 3.2-3B model represents a significant advancement in the development of large language models, combining high performance with efficient resource usage. Its multilingual capabilities and enhanced instruction-following features make it an ideal choice for developers looking to build sophisticated natural language processing applications. With its robust architecture and comprehensive training methodology, Llama 3.2-3B stands out as a powerful tool in the evolving landscape of AI technologies.
Text Summarization using T5
Text summarization using the T5 (Text-to-Text Transfer Transformer) small model is a natural language processing (NLP) task where the model generates concise summaries of input text. T5 is a transformer-based model developed by Google that treats every NLP problem as a text-to-text task.
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