Tech Tweet Generator Llama-3 (Fine-Tuned)

This model is a fine-tuned version of Llama-3.2-3B-Instruct designed to convert dense scientific and technical research paper abstracts into engaging, viral Twitter threads.

It was trained using LoRA (Low-Rank Adaptation) on the Apple MLX framework.

πŸš€ Model Description

  • Developed by: Meet Merchant
  • Base Model: mlx-community/Llama-3.2-3B-Instruct-4bit
  • Task: Summarization / Style Transfer (Research Paper Abstract -> Engaging Twitter Thread)
  • Language: English
  • Framework: MLX

πŸ’» How to Use

You can run this model locally on your Mac using mlx-lm.

Installation

pip install mlx-lm

Python Code

from mlx_lm import load, generate

# Load the model and your adapters
model, tokenizer = load(
    "mlx-community/Llama-3.2-3B-Instruct-4bit",
    adapter_path="meetmerchant/tech-tweet-generator-llama3" 
)

abstract = """
[Paste Abstract Here]
"""

prompt = f"""
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are a viral science communicator.
<|eot_id|><|start_header_id|>user<|end_header_id|>
Title: Example Paper
Abstract:
{abstract}
<|eot_id|><|start_header_id|>assistant<|end_header_id|>
"""

response = generate(model, tokenizer, prompt=prompt, max_tokens=500)
print(response)

πŸ“Š Training Details

  • Dataset: 50+ ArXiv papers from the field of AI.
  • Ground Truth: Generated by GPT-4o-mini ("Teacher-Student" distillation).
  • Training Config:
    • LoRA Rank: 16
    • Quantization: 4-bit
    • Iterations: 200

πŸ† Evaluation

The model was evaluated using LLM-as-a-Judge (GPT-4o) against the base model.

  • Win Rate: 66% (vs Base Model)
  • Strengths: High engagement, emoji usage, accessible language.
  • Weaknesses: Can occasionally hallucinate specific details if the abstract is too dense.

Built with ❀️ on a MacBook Pro M3 using Apple MLX.

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