Llama-3.2-1B-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).
Text completion
(Form Summary) In this example, we are running 'Sentiment analysis' using Llama 3.2 1B instruct. The prompt is provided with the statement to classify 'Negative' or 'Positive' sentiment
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
Sentiment-analysis
Response
Negative.
Reason:
The text expresses dissatisfaction with the product and the experience, stating that the claim was not accepted and the experience was not something the author would recommend. This indicates a negative sentiment. Note: The text does not contain any explicit negative
Explainability:
Code Completion
Fibonacci sequence is a series of numbers in which each number is thesum of the two preceding ones, usually starting with 0 and 1.
### Fibonacci Function
Here's a Python function thatcalculates the n-thFibonacci number using memoization to improve performance.
```python
def fibonacci(n, memo={}):
"""
Calculate the n-th Fibonacci number.
Args:
n (int): The position of the Fibonacci number to calculate.
memo (dict): A dictionary to store previously calculated Fibonacci numbers.
Returns:
int: The n-th Fibonacci number.
"""
if n <= 0:
return 0
elif n == 1:
return 1
elif n not in memo:
memo[n] = fibonacci(n-1, memo) + fibonacci(n-2, memo)
return memo[n]
# Example usage:
print(fibonacci(10)) # Output: 55
Explainability
Model Information
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). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks.
Model Developer: Meta
Model Architecture: Llama 3.2 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.
Supported Languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly.
Llama 3.2 Model Family: Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
Model Release Date: Sept 25, 2024
Status: This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety.
License: Use of Llama 3.2 is governed by the Llama 3.2 Community License (a custom, commercial license agreement).
Feedback: Instructions on how to provide feedback or comments on the model can be found in the Llama Models README. For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go here.
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