Instructions to use NicholasCorrado/zephyr-7b-uf-dpo-2e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NicholasCorrado/zephyr-7b-uf-dpo-2e with Transformers:

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

pipe = pipeline("text-generation", model="NicholasCorrado/zephyr-7b-uf-dpo-2e")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NicholasCorrado/zephyr-7b-uf-dpo-2e")
model = AutoModelForCausalLM.from_pretrained("NicholasCorrado/zephyr-7b-uf-dpo-2e", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NicholasCorrado/zephyr-7b-uf-dpo-2e with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NicholasCorrado/zephyr-7b-uf-dpo-2e"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NicholasCorrado/zephyr-7b-uf-dpo-2e",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NicholasCorrado/zephyr-7b-uf-dpo-2e

SGLang

How to use NicholasCorrado/zephyr-7b-uf-dpo-2e 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 "NicholasCorrado/zephyr-7b-uf-dpo-2e" \
    --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": "NicholasCorrado/zephyr-7b-uf-dpo-2e",
		"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 "NicholasCorrado/zephyr-7b-uf-dpo-2e" \
        --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": "NicholasCorrado/zephyr-7b-uf-dpo-2e",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NicholasCorrado/zephyr-7b-uf-dpo-2e with Docker Model Runner:
```
docker model run hf.co/NicholasCorrado/zephyr-7b-uf-dpo-2e
```

zephyr-7b-uf-dpo-2e

This model is a fine-tuned version of alignment-handbook/zephyr-7b-sft-full on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

Loss: 0.5017
Rewards/chosen: -1.6430
Rewards/rejected: -2.9109
Rewards/accuracies: 0.7891
Rewards/margins: 1.2679
Logps/rejected: -553.7540
Logps/chosen: -426.9287
Logits/rejected: 2.3524
Logits/chosen: 1.4100

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: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 256
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.5424	0.5021	120	0.5378	-0.5794	-1.3437	0.7578	0.7642	-397.0288	-320.5744	-0.5261	-0.7461
0.4857	1.0042	240	0.5071	-0.9384	-1.8536	0.7695	0.9153	-448.0264	-356.4662	0.8934	0.1618
0.3605	1.5063	360	0.4996	-1.5624	-2.7607	0.7734	1.1983	-538.7272	-418.8674	2.1269	1.2559

Framework versions

Transformers 4.44.1
Pytorch 2.1.2+cu121
Datasets 2.21.0
Tokenizers 0.19.1

Downloads last month: 9

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for NicholasCorrado/zephyr-7b-uf-dpo-2e

Base model

mistralai/Mistral-7B-v0.1

Finetuned

alignment-handbook/zephyr-7b-sft-full

Finetuned

(390)

this model

NicholasCorrado
/

zephyr-7b-uf-dpo-2e

zephyr-7b-uf-dpo-2e

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for NicholasCorrado/zephyr-7b-uf-dpo-2e

Dataset used to train NicholasCorrado/zephyr-7b-uf-dpo-2e