---
base_model: marcuscedricridia/kgr-600m-2511-it-616
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
- sft
license: agpl-3.0
language:
- en
datasets:
- marcuscedricridia/finetome-score-gte-4p5-only
- marcuscedricridia/wizard_vicuna_70k_unfiltered-deepclean-sharegpt
- marcuscedricridia/ultrafeedback-chosen-rating-eq-5
---

## Overview

`kgr-600m-2511-it-709` is a 600M parameter language model fine-tuned for general instruction-following tasks. It is part of the KGR family, designed to be lightweight and efficient while maintaining strong performance on practical prompts.

## Intended Use

This model is built for general-purpose instruction tasks such as:

- Question answering  
- Summarization  
- Short-form generation  
- Instruction completion  

It performs best when given clear, direct prompts.

## Inference Settings

Recommended parameters for sampling:

- `temperature = 0.3`  
- `min_p = 0.01`  
- `repetition_penalty = 1.2`  
- `top_p = 0.95`  
- `top_k = 100` (values of 20 or 40 are also valid)

A repetition penalty is used due to the model’s smaller size. It helps prevent looping and improves output coherence.

## Special Notes

- The `enable_thinking = true/false` parameter no longer affects behavior when toggled. This flag was overridden during training.
- However, the **idea behind** `enable_thinking`—encouraging chain-of-thought reasoning—is still functional when prompted explicitly. Asking the model to "think step by step" or using similar phrasing can activate this behavior.

## Limitations

- Struggles with complex multi-step reasoning.
- Not suitable for high-stakes or sensitive applications.
- Outputs may occasionally reflect training biases or limitations in generalization.