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vishal-adithya/
ministral-8B-texthumanizer

Name: ministral-8B-texthumanizer
Availability: InStock

mlx

finetune

vishal-adithya/texthumanizer-preprocessed-data

mistralai/Ministral-8B-Instruct-2410

finetune

apache-2.0

model-index

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TextHumanizer - Fine-tuned Ministral-8B-Instruct:

TextHumanizer is a fine-tuned version of the Ministral-8B-Instruct model. It is designed to transform robotic, overly-formal, or synthetic AI-generated text into fluent, natural, and human-like language.

This model was fine-tuned using Apple's MLX framework on a custom dataset of AI-generated text paired with humanized rewrites.

Model Details:

Base model: mistralai/Ministral-8B-Instruct
Model size: 8B parameters
Fine-tuned using: MLX (Apple Silicon-native training)
Fine-tuning method: QLora
Precision: float16
Hardware used: Apple Silicon M1 Macbook Pro
Training time: 10 mins
Epochs / Steps: 200
Batch size: 4

Training Metrics:

Metric	Value
Final Training Loss	`0.171`
Final Validation Loss	`0.175`

Dataset:

Source: Custom synthetic-to-human text pairs
Size: 10k rows
Structure: Pairs of (synthetic_input, humanized_output)
Preprocessing: Standard MLX tokenization, formatting for instruct tuning
License: Public

Capabilities:

TextHumanizer is designed to:

Improve fluency and tone of AI outputs
Make answers sound more relatable and natural
Polish robotic or overly formal language

MLX (Apple Silicon):

python

from mlx_lm import load, generate

model, tokenizer = load("vishal-adithya/ministral-8B-texthumanizer")
prompt = "Instruction: Make this sound more natural.Input: The individual proceeded to consume nourishment.Response:"
generate(model, tokenizer, prompt)