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Dwayne234/finetune-DO
finetune-DO is a machine learning model from Dwayne234. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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Downloads · 30 days
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Updated Apr 18, 2025
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From the Hugging Face model README
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---
license: apache-2.0
tags:
- gpt-neox
- causal-lm
- lora
- fine-tuned
- text-generation
- huggingface
model-index:
- name: finetune-DO
results: []
---
# 🚀 Fine-tuned GPT-NeoX-20B on English Quotes
This model is a fine-tuned version of [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) using LoRA (Low-Rank Adaptation) on a quotes dataset from [Abirate/english_quotes](https://huggingface.co/datasets/Abirate/english_quotes).
Fine-tuning was performed by [@Dwayne234](https://huggingface.co/Dwayne234) using Hugging Face 🤗 and Google Colab with 4-bit quantization enabled (BitsAndBytes).
---
## 🧠 Model Details
- **Base Model**: GPT-NeoX-20B
- **Adapter Type**: LoRA
- **Quantization**: 4-bit (NF4, bfloat16 compute)
- **Training Steps**: 10
- **Frameworks**: 🤗 Transformers, PEFT, BitsAndBytes
---
## 📦 How to Use
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("Dwayne234/finetune-DO")
tokenizer = AutoTokenizer.from_pretrained("Dwayne234/finetune-DO")
prompt = "Ask not what your country"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
| Setting | Value |
|---|---|
per_device_train_batch_size | 1 |
gradient_accumulation_steps | 4 |
max_steps | 10 |
learning_rate | 2e-4 |
fp16 | True |
optimizer | paged_adamw_8bit |
If you use this model, please consider citing the base model authors and giving a ⭐ to @Dwayne234!
Apache 2.0
</details>
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