A beginner's guide to the Granite-3.1-8b-Instruct model by Ibm-Granite on Replicate

#coding #ai #machinelearning #programming

This is a simplified guide to an AI model called Granite-3.1-8b-Instruct maintained by Ibm-Granite. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.

Model overview

granite-3.1-8b-instruct is a lightweight and open-source 8B parameter model designed by IBM Granite to excel in instruction following tasks. It is part of the Granite 3.1 language model family, which extends the context length of Granite 3.0 models from 4K to 128K using a progressive training strategy. This allows the models to handle longer inputs and generate more coherent and relevant responses. The Granite-3.1-8B-Instruct model outperforms similar-sized models on the Hugging Face OpenLLM Leaderboard, indicating its strong capabilities.

The Granite 3.1 model family includes both dense and sparse Mixture-of-Experts (MoE) architectures, ranging from 2B to 8B parameters, providing users with options that balance performance and compute requirements. At each scale, the models are released as both base checkpoints (after pretraining) and instruct checkpoints (finetuned for dialogue, instruction-following, helpfulness, and safety).

Model inputs and outputs

Inputs

Prompt: The text prompt that the model will use to generate a response.
System Prompt: A system-level prompt that helps guide the model's behavior, particularly for chat-like interactions.
Minimum Tokens: The minimum number of tokens the model should generate as output.
Maximum Tokens: The maximum number of tokens the model should generate as output.
Temperature: A value that modulates the model's exploration vs. exploitation during generation.
Top K: The number of highest probability tokens to consider for generating the output.
Top P: A probability threshold for generating the output, where only the top tokens with cumulative probability above this threshold are considered.
Frequency Penalty: A penalty applied to tokens based on their frequency of appearance in the generated text.
Presence Penalty: A penalty applied to tokens based on whether they have already appeared in the generated text.
Stop Sequences: A comma-separated list of sequences that will stop the generation if encountered.

Outputs

The generated text, which can be used for a variety of instruction-following tasks such as summarization, problem-solving, text translation, reasoning, code tasks, and function-calling.