metadata

license: apache-2.0
datasets:
  - Anthropic/hh-rlhf
language:
  - en
pipeline_tag: text-generation
tags:
  - text-generation-inference

Model Card for OpenBezoar-HH-RLHF-DPO

The OpenBezoar-HH-RLHF-DPO is an LLM that has been fine tuned for human preferences alignment using Direct Preference Optimization (DPO), on top of OpenBezoar-HH-RLHF-SFT model on a subset of Anthropic's HH-RLHF Dataset.

Model Details

Base Model: OpenBezoar-HH-RLHF-SFT
Dataset used for SFT: First 100K examples of the HH-RLHF dataset
Alignment Method: Direct Preference Optimization (DPO)
Epochs: 1

Model Description

OpenBezoar-HH-RLHF-SFT is an LLM that is built upon the OpenLLaMA 3B v2 architecture. This model has been fine-tuned for human preferences alignment using DPO. Alignment has been performed on top of the OpenBezoar-HH-RLHF-SFT model. For more information please refer to our paper.

Model Sources

Repository: [More Information Needed]
Paper : [More Information Needed]

Instruction Format

We follow a modified version of the Alpaca prompt template as shown below. It is important to utilize this template in order to obtain best responses for instruction related tasks.

### System:
Below is an instruction that describes a task, optionally paired with an input that provides further context following that instruction. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Response:

Notice that no end-of-sentence (eos) token is being appended.

Note: The system prompt shown in the following figure is the one that the model has been trained on most of the time. However, you may attempt to use any other system prompt that is available in the Orca scheme.

Limitations

The model might not consistently show improved abilities to follow instructions, and it could respond inappropriately or get stuck in loops.
Although this model is aligned to human preferences and has been evaluated for performance, it is not guaranteed that it will refrain from generating harmful content exclusively.
Caution is urged against relying on this model for production or adjacent use-cases.

Citation

If you find our work useful, please cite our paper as follows:

[More Information Needed]

Model Authors

Chandeepa Dissanayake, Lahiru Lowe, Sachith Gunasekara, and Yasiru Ratnayake