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

Libraries

How to use wxzhang/zephyr-7b-dpo-full with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wxzhang/zephyr-7b-dpo-full")
model = AutoModelForCausalLM.from_pretrained("wxzhang/zephyr-7b-dpo-full")
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 wxzhang/zephyr-7b-dpo-full with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/wxzhang/zephyr-7b-dpo-full

SGLang

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

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

zephyr-7b-dpo-full

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5107
Rewards/chosen: -1.4645
Rewards/rejected: -2.3555
Rewards/accuracies: 0.7718
Rewards/margins: 0.8911
Logps/rejected: -491.4778
Logps/chosen: -426.3907
Logits/rejected: 1.4587
Logits/chosen: 0.9514

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: 4
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 4
total_train_batch_size: 64
total_eval_batch_size: 32
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
0.6339	0.1	100	0.6366	-0.4251	-0.6280	0.6766	0.2029	-318.7289	-322.4543	-1.7266	-1.8550
0.5801	0.21	200	0.5761	-0.9339	-1.4916	0.7242	0.5577	-405.0862	-373.3335	-1.7791	-1.8866
0.5298	0.31	300	0.5505	-0.9519	-1.6203	0.7401	0.6684	-417.9537	-375.1365	-0.9729	-1.1938
0.5055	0.42	400	0.5331	-1.3809	-2.1858	0.7540	0.8048	-474.5050	-418.0395	0.2901	-0.0376
0.5243	0.52	500	0.5240	-1.5398	-2.3578	0.7718	0.8180	-491.7054	-433.9210	1.1167	0.7245
0.5024	0.63	600	0.5212	-1.6677	-2.5319	0.75	0.8643	-509.1215	-446.7127	1.3224	0.8469
0.4855	0.73	700	0.5156	-1.5293	-2.4112	0.7579	0.8819	-497.0490	-432.8780	1.5165	1.0177
0.5048	0.84	800	0.5121	-1.4754	-2.3714	0.7698	0.8960	-493.0640	-427.4831	1.3869	0.8797
0.5193	0.94	900	0.5109	-1.4545	-2.3434	0.7738	0.8889	-490.2650	-425.3930	1.4499	0.9411

Framework versions

Transformers 4.36.2
Pytorch 2.1.2
Datasets 2.14.6
Tokenizers 0.15.0

Downloads last month: 1

Safetensors

Model size

7B params

Tensor type

BF16