Instructions to use moreh/MoMo-72B-lora-1.8.7-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use moreh/MoMo-72B-lora-1.8.7-DPO with Transformers:

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

pipe = pipeline("text-generation", model="moreh/MoMo-72B-lora-1.8.7-DPO")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("moreh/MoMo-72B-lora-1.8.7-DPO")
model = AutoModelForMultimodalLM.from_pretrained("moreh/MoMo-72B-lora-1.8.7-DPO")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use moreh/MoMo-72B-lora-1.8.7-DPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "moreh/MoMo-72B-lora-1.8.7-DPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moreh/MoMo-72B-lora-1.8.7-DPO",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/moreh/MoMo-72B-lora-1.8.7-DPO

SGLang

How to use moreh/MoMo-72B-lora-1.8.7-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 "moreh/MoMo-72B-lora-1.8.7-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": "moreh/MoMo-72B-lora-1.8.7-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 "moreh/MoMo-72B-lora-1.8.7-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": "moreh/MoMo-72B-lora-1.8.7-DPO",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use moreh/MoMo-72B-lora-1.8.7-DPO with Docker Model Runner:
```
docker model run hf.co/moreh/MoMo-72B-lora-1.8.7-DPO
```

MoMo-72B-lora-1.8.7-DPO / README.md

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	---
	license: mit
	language:
	- en
	metrics:
	- accuracy
	library_name: transformers
	---

	# 24/04/05 update
	We introduce [Moreh AI Model Hub with AMD GPU](https://model-hub.moreh.io/), an ai model host platform powered by AMD MI250 GPUs.
	You can now test live-inference of this model at Moreh AI Model Hub.

	# Introduction
	MoMo-72B-lora-1.8.7-DPO is trained via Direct Preference Optimization([DPO](https://arxiv.org/abs/2305.18290)) from [MoMo-72B-LoRA-V1.4](https://huggingface.co/moreh/MoMo-72B-LoRA-V1.4) as its base model, with several optimizations in hyperparameters.
	[MoMo-72B-LoRA-V1.4](https://huggingface.co/moreh/MoMo-72B-LoRA-V1.4) is trained via Supervised Fine-Tuning (SFT) using [LoRA](https://arxiv.org/abs/2106.09685), with the QWEN-72B model as its base-model.
	Note that we did not exploit any form of weight merge.
	For leaderboard submission, the trained weight is realigned for compatibility with llama.
	MoMo-72B is trained using [Moreh](https://moreh.io/)'s [MoAI platform](https://moreh.io/product), which simplifies the training of large-scale models, and AMD's MI250 GPU.
	#

	## Details
	### Used Librarys
	- torch
	- peft
	### Used Datasets
	- [slimorca](Open-Orca/SlimOrca)
	- [truthy](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1)
	- [orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
	- No other dataset was used
	- No benchmark test set or the training set are used
	- [data contamination check](https://github.com/swj0419/detect-pretrain-code-contamination) result

	\| Model \| ARC \| MMLU \| TruthfulQA \| GSM8K \|
	\|------------------------------\|-------\|-------\|-------\|-------\|
	\| V1.8.7(result < 0.1, %)\| TBU \|TBU \| 0.44 \| 0.47 \|
	### Used Environments
	- AMD MI250 & MoAI platform
	- Please visit https://moreh.io/product for more information about MoAI platform
	- Or, contact us directly [contact@moreh.io](mailto:contact@moreh.io)

	## How to use

	```python
	# pip install transformers==4.35.2
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	tokenizer = AutoTokenizer.from_pretrained("moreh/MoMo-72B-lora-1.8.7-DPO")
	model = AutoModelForCausalLM.from_pretrained(
	"moreh/MoMo-72B-lora-1.8.7-DPO"
	)
	```