metadata

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

Mathmate-7B-DELLA-ORPO-C

Mathmate-7B-DELLA-ORPO-C is a LoRA adapter for Haleshot/Mathmate-7B-DELLA-ORPO, finetuned to improve performance on everyday conversations.

Model Details

Base Model: Haleshot/Mathmate-7B-DELLA
Training Dataset: HuggingFaceTB/everyday-conversations-llama3.1-2k

Dataset

The model was finetuned on the HuggingFaceTB/everyday-conversations-llama3.1-2k dataset, which focuses on everyday conversations and small talk.

Usage

To use this LoRA adapter, you need to load both the base model and the adapter. Here's an example:

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel, PeftConfig
import torch

base_model_name = "Haleshot/Mathmate-7B-DELLA"
adapter_name = "Haleshot/Mathmate-7B-DELLA-ORPO-C"

base_model = AutoModelForCausalLM.from_pretrained(base_model_name, torch_dtype=torch.float16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
model = PeftModel.from_pretrained(base_model, adapter_name)

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 the weather today."
response = generate_response(prompt)
print(response)

Acknowledgements

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