README.md

---
base_model: Haleshot/Mathmate-7B-DELLA-ORPO
tags:
- finetuned
- orpo
- everyday-conversations
datasets:
- HuggingFaceTB/everyday-conversations-llama3.1-2k
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
---

# Mathmate-7B-DELLA-ORPO-D

Mathmate-7B-DELLA-ORPO-D is a finetuned version of [Haleshot/Mathmate-7B-DELLA-ORPO](https://huggingface.co./Haleshot/Mathmate-7B-DELLA-ORPO) using the ORPO method, combined with a LoRA adapter trained on everyday conversations.

## Model Details

- **Base Model:** [Haleshot/Mathmate-7B-DELLA-ORPO](https://huggingface.co./Haleshot/Mathmate-7B-DELLA-ORPO)
- **Training Dataset:** [HuggingFaceTB/everyday-conversations-llama3.1-2k](https://huggingface.co./datasets/HuggingFaceTB/everyday-conversations-llama3.1-2k)

## Dataset

The model incorporates training on the [HuggingFaceTB/everyday-conversations-llama3.1-2k](https://huggingface.co./datasets/HuggingFaceTB/everyday-conversations-llama3.1-2k) dataset, which focuses on everyday conversations and small talk.

## Usage

Here's an example of how to use the model:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "Haleshot/Mathmate-7B-DELLA-ORPO-ORPO-D"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")

def generate_response(prompt, max_length=512):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_length=max_length, num_return_sequences=1, do_sample=True, temperature=0.7)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

prompt = "Let's have a casual conversation about weekend plans."
response = generate_response(prompt)
print(response)
```

## Acknowledgements

Thanks to the HuggingFaceTB team for providing the everyday conversations dataset used in this finetuning process.