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 Settings

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

File size: 829 Bytes

ce72571

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    "eval_logits/chosen": -0.9673975706100464,
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    "eval_logps/chosen": -338.55499267578125,
    "eval_logps/rejected": -360.69610595703125,
    "eval_loss": 0.5627262592315674,
    "eval_rewards/accuracies": 0.7329999804496765,
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    "eval_rewards/margins": 0.5175721645355225,
    "eval_rewards/rejected": -0.8966532945632935,
    "eval_runtime": 561.3254,
    "eval_samples": 2000,
    "eval_samples_per_second": 3.563,
    "eval_steps_per_second": 0.891,
    "total_flos": 0.0,
    "train_loss": 0.5879578035582537,
    "train_runtime": 40532.5341,
    "train_samples": 61134,
    "train_samples_per_second": 1.508,
    "train_steps_per_second": 0.012
}