Instructions to use NoManDeRY/DPO-Shift-Llama-3-8B-Ultrafeedback-decrease_linear-1.0to0.95 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NoManDeRY/DPO-Shift-Llama-3-8B-Ultrafeedback-decrease_linear-1.0to0.95 with Transformers:

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

pipe = pipeline("text-generation", model="NoManDeRY/DPO-Shift-Llama-3-8B-Ultrafeedback-decrease_linear-1.0to0.95")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NoManDeRY/DPO-Shift-Llama-3-8B-Ultrafeedback-decrease_linear-1.0to0.95")
model = AutoModelForCausalLM.from_pretrained("NoManDeRY/DPO-Shift-Llama-3-8B-Ultrafeedback-decrease_linear-1.0to0.95")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use NoManDeRY/DPO-Shift-Llama-3-8B-Ultrafeedback-decrease_linear-1.0to0.95 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NoManDeRY/DPO-Shift-Llama-3-8B-Ultrafeedback-decrease_linear-1.0to0.95"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NoManDeRY/DPO-Shift-Llama-3-8B-Ultrafeedback-decrease_linear-1.0to0.95",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NoManDeRY/DPO-Shift-Llama-3-8B-Ultrafeedback-decrease_linear-1.0to0.95

SGLang

How to use NoManDeRY/DPO-Shift-Llama-3-8B-Ultrafeedback-decrease_linear-1.0to0.95 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 "NoManDeRY/DPO-Shift-Llama-3-8B-Ultrafeedback-decrease_linear-1.0to0.95" \
    --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": "NoManDeRY/DPO-Shift-Llama-3-8B-Ultrafeedback-decrease_linear-1.0to0.95",
		"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 "NoManDeRY/DPO-Shift-Llama-3-8B-Ultrafeedback-decrease_linear-1.0to0.95" \
        --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": "NoManDeRY/DPO-Shift-Llama-3-8B-Ultrafeedback-decrease_linear-1.0to0.95",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NoManDeRY/DPO-Shift-Llama-3-8B-Ultrafeedback-decrease_linear-1.0to0.95 with Docker Model Runner:
```
docker model run hf.co/NoManDeRY/DPO-Shift-Llama-3-8B-Ultrafeedback-decrease_linear-1.0to0.95
```

DPO-Shift-Llama-3-8B-Ultrafeedback-decrease_linear-1.0to0.95 / README.md

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	---
	library_name: transformers
	base_model: princeton-nlp/Llama-3-Base-8B-SFT
	tags:
	- alignment-handbook
	- generated_from_trainer
	datasets:
	- HuggingFaceH4/ultrafeedback_binarized
	model-index:
	- name: llama-3-8b-dpo-ultrafeedback-decrease_linear-1.0to0.95
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# llama-3-8b-dpo-ultrafeedback-decrease_linear-1.0to0.95

	This is a model released from the preprint: [DPO-Shift: Shifting the Distribution of Direct Preference Optimization](https://arxiv.org/abs/2502.07599). Please refer to our [repository](https://github.com/Meaquadddd/DPO-Shift) for more details.


	This model is a fine-tuned version of [princeton-nlp/Llama-3-Base-8B-SFT](https://huggingface.co/princeton-nlp/Llama-3-Base-8B-SFT) on the HuggingFaceH4/ultrafeedback_binarized dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.5619
	- Rewards/chosen: -0.3784
	- Rewards/rejected: -0.8957
	- Dpo Lambda: 0.9528
	- Rewards/accuracies: 0.7310
	- Rewards/margins: 0.5173
	- Logps/rejected: -360.6006
	- Logps/chosen: -338.4835
	- Logits/rejected: -1.0030
	- Logits/chosen: -0.9672

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 5e-07
	- train_batch_size: 2
	- eval_batch_size: 2
	- seed: 42
	- distributed_type: multi-GPU
	- num_devices: 2
	- gradient_accumulation_steps: 32
	- total_train_batch_size: 128
	- total_eval_batch_size: 4
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: cosine
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 1

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Dpo Lambda \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \|
	\|:-------------:\|:------:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:----------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\|
	\| 0.6826 \| 0.1047 \| 50 \| 0.6803 \| 0.0669 \| 0.0399 \| 0.9948 \| 0.6690 \| 0.0270 \| -267.0431 \| -293.9557 \| -0.9094 \| -0.8412 \|
	\| 0.5951 \| 0.2094 \| 100 \| 0.6223 \| -0.0861 \| -0.2745 \| 0.9895 \| 0.7130 \| 0.1884 \| -298.4850 \| -309.2591 \| -0.9195 \| -0.8667 \|
	\| 0.6296 \| 0.3141 \| 150 \| 0.5972 \| -0.2312 \| -0.5289 \| 0.9843 \| 0.7100 \| 0.2977 \| -323.9177 \| -323.7625 \| -0.9008 \| -0.8554 \|
	\| 0.6219 \| 0.4187 \| 200 \| 0.5784 \| -0.4096 \| -0.8051 \| 0.9790 \| 0.7310 \| 0.3955 \| -351.5381 \| -341.6022 \| -0.9313 \| -0.8927 \|
	\| 0.5738 \| 0.5234 \| 250 \| 0.5685 \| -0.4338 \| -0.8864 \| 0.9738 \| 0.7260 \| 0.4526 \| -359.6707 \| -344.0276 \| -0.9691 \| -0.9333 \|
	\| 0.5598 \| 0.6281 \| 300 \| 0.5695 \| -0.4246 \| -0.9086 \| 0.9686 \| 0.7220 \| 0.4840 \| -361.8922 \| -343.1057 \| -1.0002 \| -0.9608 \|
	\| 0.566 \| 0.7328 \| 350 \| 0.5613 \| -0.3470 \| -0.8404 \| 0.9633 \| 0.7260 \| 0.4934 \| -355.0737 \| -335.3493 \| -0.9958 \| -0.9592 \|
	\| 0.5423 \| 0.8375 \| 400 \| 0.5613 \| -0.3837 \| -0.8996 \| 0.9581 \| 0.7290 \| 0.5159 \| -360.9908 \| -339.0213 \| -1.0033 \| -0.9665 \|
	\| 0.5357 \| 0.9422 \| 450 \| 0.5619 \| -0.3784 \| -0.8957 \| 0.9528 \| 0.7310 \| 0.5173 \| -360.6006 \| -338.4835 \| -1.0030 \| -0.9672 \|


	### Framework versions

	- Transformers 4.44.2
	- Pytorch 2.4.0+cu121
	- Datasets 2.21.0
	- Tokenizers 0.19.1