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---
license: mit
base_model: gpt2
tags:
- text-generation
- reddit
- social-media
- gpt2
language:
- en
datasets:
- custom
widget:
- text: "What I learned after 5 years of programming:"
- text: "TIL that"
- text: "DAE think that"
---
# Reddit Text Generation Model
This is a GPT-2 based model fine-tuned on Reddit posts to generate Reddit-style content.
## Model Details
- **Architecture**: GPT-2 (125M parameters)
- **Training Data**: Reddit posts with 1000+ upvotes
- **Use Cases**: Generate Reddit-style text, social media content
- **Training Framework**: HuggingFace Transformers + DeepSpeed
## Usage
```python
from transformers import GPT2LMHeadModel, GPT2TokenizerFast
import torch
# Load model and tokenizer
model_name = "chimcis/reddit-text-model"
tokenizer = GPT2TokenizerFast.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)
# Generate text
prompt = "What I learned after 5 years of programming:"
inputs = tokenizer.encode(prompt, return_tensors='pt')
with torch.no_grad():
outputs = model.generate(
inputs,
max_length=200,
temperature=0.8,
do_sample=True,
top_p=0.9,
pad_token_id=tokenizer.eos_token_id
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)
```
## Training Details
- **Training Steps**: 50,000
- **Batch Size**: 64 (global)
- **Learning Rate**: 1e-4
- **Hardware**: 4x NVIDIA H100 80GB
- **Training Time**: ~5 hours
## Limitations
- Model generates general Reddit-style content
- May produce inconsistent or off-topic text
- Should not be used for harmful content generation
## Citation
```
@misc{reddit-text-model,
author = {Reddit Model},
title = {Reddit Text Generation Model},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/chimcis/reddit-text-model}
}
```
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