Instructions to use SurgeGlobal/OpenBezoar-HH-RLHF-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SurgeGlobal/OpenBezoar-HH-RLHF-DPO with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SurgeGlobal/OpenBezoar-HH-RLHF-DPO")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SurgeGlobal/OpenBezoar-HH-RLHF-DPO")
model = AutoModelForCausalLM.from_pretrained("SurgeGlobal/OpenBezoar-HH-RLHF-DPO")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SurgeGlobal/OpenBezoar-HH-RLHF-DPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SurgeGlobal/OpenBezoar-HH-RLHF-DPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SurgeGlobal/OpenBezoar-HH-RLHF-DPO",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SurgeGlobal/OpenBezoar-HH-RLHF-DPO

SGLang

How to use SurgeGlobal/OpenBezoar-HH-RLHF-DPO with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SurgeGlobal/OpenBezoar-HH-RLHF-DPO" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SurgeGlobal/OpenBezoar-HH-RLHF-DPO",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SurgeGlobal/OpenBezoar-HH-RLHF-DPO" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SurgeGlobal/OpenBezoar-HH-RLHF-DPO",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SurgeGlobal/OpenBezoar-HH-RLHF-DPO with Docker Model Runner:
```
docker model run hf.co/SurgeGlobal/OpenBezoar-HH-RLHF-DPO
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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: Bitbucket Project
Paper : Pre-Print

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.

Usage

from peft import PeftConfig, PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, AutoModelForSeq2SeqLM

checkpoint =  "SurgeGlobal/OpenBezoar-HH-RLHF-DPO"

tokenizer = AutoTokenizer.from_pretrained(checkpoint)

model = AutoModelForCausalLM.from_pretrained(
    checkpoint,
    load_in_4bit=True, # optionally for low resource environments
    device_map="auto"
)

prompt =  """### 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:""".format(
    instruction="What is the world state in the year 1597."
)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=1024, do_sample=True)

print(tokenizer.decode(outputs[0]))

Evaluations

Refer to our self-reported evaluations in our paper (Section 4).

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:

@misc{surge2024openbezoar,
      title={OpenBezoar: Small, Cost-Effective and Open Models Trained on Mixes of Instruction Data}, 
      author={Chandeepa Dissanayake and Lahiru Lowe and Sachith Gunasekara and Yasiru Ratnayake},
      year={2024},
      eprint={2404.12195},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}