mjpsm/activity-generation-model-v0.1

TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:0.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 5, 2026Architecture:Transformer Featherless Exclusive Cold

The mjpsm/activity-generation-model-v0.1 is a 0.5 billion parameter model designed to generate a single next educational activity based on a student's knowledge submission. It outputs structured JSON containing activity details like title, description, instructions, type, and estimated duration. This model is specifically trained for educational activity generation, distinguishing it from general-purpose language models by its focused output and domain-specific application.

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Activity Generation Model v0.1 Overview

The mjpsm/activity-generation-model-v0.1 is a specialized 0.5 billion parameter model engineered to generate a single, subsequent educational activity from a student's knowledge submission. Its core function is to provide structured output, making it distinct from general-purpose large language models.

Key Capabilities

  • Generates educational activities: Produces a single, relevant activity based on student input.
  • Structured JSON output: Returns a JSON object with specific fields:
    • title: The activity's title.
    • description: A brief explanation of the activity.
    • instructions: Steps for the student to follow.
    • activityType: Categorizes the activity (e.g., REFLECTION, RESEARCH, CREATE, PRACTICE).
    • estimatedMinutes: An approximate duration for the activity.

Training and Limitations

This model is an early proof-of-concept, trained on a relatively small dataset of approximately 200 synthetic input-output examples. Due to its limited training data, it has several known limitations:

  • May exhibit a bias towards common activity types like CREATE or PRACTICE.
  • Time estimates (estimatedMinutes) might be inaccurate.
  • Could occasionally produce malformed JSON output.

Should I use this for my use case?

This model is ideal for developers and educators looking to automate the generation of specific, structured educational activities. If your application requires a model that can interpret student submissions and suggest a next learning step in a programmatic, JSON-formatted way, this model is a strong candidate. However, given its proof-of-concept status and small training dataset, it is best suited for initial prototyping, non-critical applications, or as a base for further fine-tuning with more extensive and balanced data. For production systems requiring high accuracy, diverse activity types, and robust JSON output, further development or a more extensively trained model might be necessary.

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