Instructions to use NicholasCorrado/zephyr-7b-group-dpo-full-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NicholasCorrado/zephyr-7b-group-dpo-full-4 with Transformers:

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

pipe = pipeline("text-generation", model="NicholasCorrado/zephyr-7b-group-dpo-full-4")
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-group-dpo-full-4")
model = AutoModelForCausalLM.from_pretrained("NicholasCorrado/zephyr-7b-group-dpo-full-4", 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-group-dpo-full-4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NicholasCorrado/zephyr-7b-group-dpo-full-4"
# 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-group-dpo-full-4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NicholasCorrado/zephyr-7b-group-dpo-full-4

SGLang

How to use NicholasCorrado/zephyr-7b-group-dpo-full-4 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-group-dpo-full-4" \
    --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-group-dpo-full-4",
		"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-group-dpo-full-4" \
        --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-group-dpo-full-4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NicholasCorrado/zephyr-7b-group-dpo-full-4 with Docker Model Runner:
```
docker model run hf.co/NicholasCorrado/zephyr-7b-group-dpo-full-4
```

zephyr-7b-group-dpo-full-4

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: 0.3329
Rewards/chosen: -3.5718
Rewards/rejected: -6.6220
Rewards/accuracies: 0.8281
Rewards/margins: 3.0502
Logps/rejected: -952.0885
Logps/chosen: -639.7113
Logits/rejected: -0.6186
Logits/chosen: -1.1189

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: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.2094	0.2303	100	0.4273	-1.2973	-2.4710	0.7768	1.1737	-536.9892	-412.2621	-2.8747	-2.8886
0.1232	0.4606	200	0.3541	-2.8531	-5.1625	0.8069	2.3094	-806.1392	-567.8411	-1.2949	-1.6181
0.1	0.6908	300	0.3383	-3.9067	-6.9476	0.8158	3.0409	-984.6500	-673.2026	-0.4879	-0.9150
0.0956	0.9211	400	0.3329	-3.5971	-6.6557	0.8281	3.0586	-955.4623	-642.2396	-0.6052	-1.1035

Framework versions

Transformers 4.44.1
Pytorch 2.1.2+cu121
Datasets 2.21.0
Tokenizers 0.19.1

Downloads last month: 11

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for NicholasCorrado/zephyr-7b-group-dpo-full-4

Base model

mistralai/Mistral-7B-v0.1

Finetuned

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

Finetuned

(390)

this model

NicholasCorrado
/

zephyr-7b-group-dpo-full-4

zephyr-7b-group-dpo-full-4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for NicholasCorrado/zephyr-7b-group-dpo-full-4

Dataset used to train NicholasCorrado/zephyr-7b-group-dpo-full-4