Instructions to use UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2 with Transformers:

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

pipe = pipeline("text-generation", model="UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2")
model = AutoModelForCausalLM.from_pretrained("UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2

SGLang

How to use UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2 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 "UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2 with Docker Model Runner:
```
docker model run hf.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2
```

Gemma-2-9B-It-SPPO-Iter2 / README.md

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Adding Evaluation Results

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	---
	language:
	- en
	license: gemma
	datasets:
	- openbmb/UltraFeedback
	pipeline_tag: text-generation
	model-index:
	- name: Gemma-2-9B-It-SPPO-Iter2
	results:
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: IFEval (0-Shot)
	type: HuggingFaceH4/ifeval
	args:
	num_few_shot: 0
	metrics:
	- type: inst_level_strict_acc and prompt_level_strict_acc
	value: 31.0
	name: strict accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: BBH (3-Shot)
	type: BBH
	args:
	num_few_shot: 3
	metrics:
	- type: acc_norm
	value: 42.17
	name: normalized accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MATH Lvl 5 (4-Shot)
	type: hendrycks/competition_math
	args:
	num_few_shot: 4
	metrics:
	- type: exact_match
	value: 0.0
	name: exact match
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: GPQA (0-shot)
	type: Idavidrein/gpqa
	args:
	num_few_shot: 0
	metrics:
	- type: acc_norm
	value: 11.3
	name: acc_norm
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MuSR (0-shot)
	type: TAUR-Lab/MuSR
	args:
	num_few_shot: 0
	metrics:
	- type: acc_norm
	value: 10.94
	name: acc_norm
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU-PRO (5-shot)
	type: TIGER-Lab/MMLU-Pro
	config: main
	split: test
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 31.89
	name: accuracy
	source:
	url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2
	name: Open LLM Leaderboard
	---
	Self-Play Preference Optimization for Language Model Alignment (https://arxiv.org/abs/2405.00675)

	# Gemma-2-9B-It-SPPO-Iter2

	This model was developed using [Self-Play Preference Optimization](https://arxiv.org/abs/2405.00675) at iteration 2, based on the [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) architecture as starting point. We utilized the prompt sets from the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, splited to 3 parts for 3 iterations by [snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset](https://huggingface.co/datasets/snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset). All responses used are synthetic.

	Terms of Use: [Terms](https://www.kaggle.com/models/google/gemma/license/consent/verify/huggingface?returnModelRepoId=google/gemma-2-9b-it)


	## Links to Other Models
	- [Gemma-2-9B-It-SPPO-Iter1](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter1)
	- [Gemma-2-9B-It-SPPO-Iter2](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2)
	- [Gemma-2-9B-It-SPPO-Iter3](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3)

	### Model Description

	- Model type: A 8B parameter GPT-like model fine-tuned on synthetic datasets.
	- Language(s) (NLP): Primarily English
	- License: Apache-2.0
	- Finetuned from model: google/gemma-2-9b-it


	## [AlpacaEval Leaderboard Evaluation Results](https://tatsu-lab.github.io/alpaca_eval/)


	\| Model \| LC. Win Rate \| Win Rate \| Avg. Length \|
	\|-------------------------------------------\|:------------:\|:--------:\|:-----------:\|
	\|[Llama-3-8B-SPPO Iter1](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter1) \|48.70 \|40.76 \| 1669
	\|[Llama-3-8B-SPPO Iter2](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2) \|50.93 \| 44.64 \| 1759
	\|[Llama-3-8B-SPPO Iter3](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3) \|53.27 \|47.74 \| 1803






	### Training hyperparameters
	The following hyperparameters were used during training:

	- learning_rate: 5e-07
	- eta: 1000
	- per_device_train_batch_size: 8
	- gradient_accumulation_steps: 1
	- seed: 42
	- distributed_type: deepspeed_zero3
	- num_devices: 8
	- optimizer: RMSProp
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.1
	- num_train_epochs: 1.0




	## Citation
	```
	@misc{wu2024self,
	title={Self-Play Preference Optimization for Language Model Alignment},
	author={Wu, Yue and Sun, Zhiqing and Yuan, Huizhuo and Ji, Kaixuan and Yang, Yiming and Gu, Quanquan},
	year={2024},
	eprint={2405.00675},
	archivePrefix={arXiv},
	primaryClass={cs.LG}
	}
	```


	# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
	Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_UCLA-AGI__Gemma-2-9B-It-SPPO-Iter2)

	\| Metric \|Value\|
	\|-------------------\|----:\|
	\|Avg. \|21.22\|
	\|IFEval (0-Shot) \|31.00\|
	\|BBH (3-Shot) \|42.17\|
	\|MATH Lvl 5 (4-Shot)\| 0.00\|
	\|GPQA (0-shot) \|11.30\|
	\|MuSR (0-shot) \|10.94\|
	\|MMLU-PRO (5-shot) \|31.89\|