A beginner's guide to the Granite-3.1-2b-Instruct model by Ibm-Granite on Replicate
Published: (January 4, 2026 at 10:31 PM EST)
2 min read
Source: Dev.to
Source: Dev.to
Overview
Granite‑3.1‑2b‑Instruct is an open‑source language model maintained by ibm‑granite. It builds on its predecessor granite‑3.0‑2b‑instruct, extending the context length from 4 K to 128 K tokens while maintaining a balance between computational efficiency and performance. The model is part of the Granite‑3.1 family, which also includes larger variants such as granite‑3.1‑8b‑instruct, offering options for different computational needs.
Model Details
- Architecture: Decoder‑only transformer
- Parameter count: 2 billion
- Context window: Up to 128 K tokens
- License: Open source (check the repository for the exact license)
The model accepts text‑based prompts and generates human‑like responses through a chat‑style interface. It processes inputs using a system prompt that guides its behavior.
Prompting Parameters
| Parameter | Description | Default |
|---|---|---|
| Prompt | Main text input for the model to respond to | – |
| System Prompt | Guides model behavior (e.g., “You are a helpful assistant”) | “You are a helpful assistant” |
| Temperature | Controls output randomness; higher values produce more diverse text | 0.6 |
| Max Tokens | Upper bound for the length of the generated output | – |
| Min Tokens | Lower bound for the length of the generated output | – |
| Top K / Top P | Parameters for controlling token selection during sampling | – |
| Frequency Penalty | Reduces repetition of frequently occurring tokens | – |
| Presence Penalty | Encourages the model to introduce new tokens not yet present in the output | – |
Features
- Text Generation: Produces text responses in an array format suitable for downstream processing.
- Context‑Aware Responses: Maintains conversation context when used in a chat format, allowing for multi‑turn interactions.
- Instruction Following: Designed to understand and execute a wide range of user instructions with reasonable accuracy.
Usage Tips
- Set a clear system prompt to define the assistant’s role and tone.
- Adjust temperature based on the desired creativity: lower values for deterministic answers, higher values for more varied output.
- Use top‑K/top‑P sampling to fine‑tune the balance between coherence and diversity.
- Apply frequency and presence penalties when you notice repetitive or overly generic responses.
For more detailed information, refer to the official Granite‑3.1‑2b‑Instruct repository and documentation.