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
```

Add pipeline tag and Github link

by nielsr HF Staff - opened Jun 9, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-5

Files changed (1) hide show

README.md +6 -5

README.md CHANGED Viewed

@@ -1,11 +1,12 @@
 ---
-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: []
@@ -16,7 +17,7 @@ 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.
@@ -83,4 +84,4 @@ The following hyperparameters were used during training:
 - Transformers 4.44.2
 - Pytorch 2.4.0+cu121
 - Datasets 2.21.0
-- Tokenizers 0.19.1

 ---
 base_model: princeton-nlp/Llama-3-Base-8B-SFT
+datasets:
+- HuggingFaceH4/ultrafeedback_binarized
+library_name: transformers
 tags:
 - alignment-handbook
 - generated_from_trainer
+pipeline_tag: text-generation
 model-index:
 - name: llama-3-8b-dpo-ultrafeedback-decrease_linear-1.0to0.95
   results: []
 # llama-3-8b-dpo-ultrafeedback-decrease_linear-1.0to0.95
+This model is released from the preprint: [DPO-Shift: Shifting the Distribution of Direct Preference Optimization](https://arxiv.org/abs/2502.07599).  For more details, please refer to our [repository](https://github.com/Meaquadddd/DPO-Shift).
 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.
 - Transformers 4.44.2
 - Pytorch 2.4.0+cu121
 - Datasets 2.21.0
+- Tokenizers 0.19.1