qwen2.5-0.5b-reddit-next-post-lora

Qwen2.5-0.5B Reddit Next-Post LoRA

These are the LoRA adaptation weights for Qwen/Qwen2.5-0.5B, trained by Ruggero Marino Lazzaroni at the IDea_Lab (University of Graz) on the idealab-cs2/reddit_task_predict_next_k5 dataset in order to imitate Reddit user behavior: given a user's most recent posts, the model predicts the title and text of their next post.

This was the best variant out of a 16-model grid (SmolLM-360M / Qwen2.5-0.5B, base & Instruct, full fine-tune & LoRA, constant & cosine LR), selected by adjusted semantic similarity between generated and ground-truth next posts.

Model Details

• Base Model: Qwen/Qwen2.5-0.5B
• Training Method: SFT with LoRA (PEFT)
• LoRA config: r=16, alpha=32, dropout=0.1, all-linear targets
• Training Data: 5,980 users from idealab-cs2/reddit_task_predict_next_k5 (predict next post from k=5 most recent posts)
• Epochs: 10 (checkpoint from epoch 8, best eval loss)
• Learning rate: 2e-4, constant schedule
• Effective batch size: 256 (32 × 8 grad accum)
• Trained By: Ruggero Marino Lazzaroni (@ruggsea), IDea_Lab, University of Graz
• Date: 2025-06

Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B", device_map="auto")
model = PeftModel.from_pretrained(base, "idealab-cs2/qwen2.5-0.5b-reddit-next-post-lora")
tokenizer = AutoTokenizer.from_pretrained("idealab-cs2/qwen2.5-0.5b-reddit-next-post-lora")

Prompt format

The model expects a flat prompt (no chat template) with this exact structure — fill in the user's recent posts, newest last, and stop after the final cue:

You are an expert assistant that predicts Reddit posts. Your task is to generate the content for the single next post based on a user's history. The response must contain only the title and text of the predicted post, adhering strictly to the format. Do not add any conversational text, explanations, or extra posts.

Given the following posts:

REDDIT POST TITLE 1: {title of post 1}
REDDIT POST TEXT 1: {text of post 1}

REDDIT POST TITLE 2: {title of post 2}
REDDIT POST TEXT 2: {text of post 2}

Predict the title and text for the single next post. Your response must begin directly with the cue below and contain nothing else.

REDDIT POST TITLE 3:

The model generates the title, then REDDIT POST TEXT 3: ... with the post body. Renumber the cues according to how many context posts you provide (the next post is always N+1). Prioritize the most recent posts if you need to truncate for context length.

Generation example

prompt = """You are an expert assistant that predicts Reddit posts. Your task is to generate the content for the single next post based on a user's history. The response must contain only the title and text of the predicted post, adhering strictly to the format. Do not add any conversational text, explanations, or extra posts.

Given the following posts:

REDDIT POST TITLE 1: My cat learned to open the fridge
REDDIT POST TEXT 1: She's 4kg of pure menace. Caught her at 3am eating the cheese.

REDDIT POST TITLE 2: Update: fridge cat has been thwarted
REDDIT POST TEXT 2: Child lock installed. She now screams at the fridge instead.

Predict the title and text for the single next post. Your response must begin directly with the cue below and contain nothing else.

REDDIT POST TITLE 3: """

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Evaluation

Notes from the grid search: base (non-Instruct) models massively outperformed their Instruct counterparts on this task (Instruct variants had 65-75% parse failure rates), and LoRA generalized better than full fine-tuning, which overfit after ~4 epochs.

Citation

If you use this model, please cite it as:

@misc{lazzaroni2025redditnextpost,
  author = {Lazzaroni, Ruggero Marino},
  title = {Qwen2.5-0.5B Reddit Next-Post LoRA},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/idealab-cs2/qwen2.5-0.5b-reddit-next-post-lora}}
}

License

This model is available under the same license as the base model (Qwen/Qwen2.5-0.5B, apache-2.0).