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

Libraries

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

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

pipe = pipeline("text-generation", model="lewtun/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("lewtun/zephyr-7b-dpo-full")
model = AutoModelForCausalLM.from_pretrained("lewtun/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 lewtun/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 "lewtun/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": "lewtun/zephyr-7b-dpo-full",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

zephyr-7b-dpo-full

This model is a fine-tuned version of lewtun/zephyr-7b-sft-full on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4949
Rewards/chosen: -1.0109
Rewards/rejected: -1.9949
Rewards/accuracies: 0.7891
Rewards/margins: 0.9840
Logps/rejected: -458.5204
Logps/chosen: -380.6149
Logits/rejected: 1.7815
Logits/chosen: 0.4467

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: 2
total_train_batch_size: 128
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	Logits/chosen	Logits/rejected	Logps/chosen	Logps/rejected	Validation Loss	Rewards/accuracies	Rewards/chosen	Rewards/margins	Rewards/rejected
0.5594	0.21	100	-0.4568	0.0349	-345.0751	-391.4898	0.5459	0.7695	-0.6555	0.6691	-1.3246
0.5342	0.42	200	0.1644	1.0433	-360.3595	-414.7491	0.5221	0.8047	-0.8084	0.7489	-1.5572
0.5085	0.63	300	0.6383	1.9502	-383.1190	-455.6415	0.5045	0.7891	-1.0360	0.9302	-1.9662
0.4998	0.84	400	0.4907	1.8212	-384.5488	-461.4449	0.4953	0.7930	-1.0503	0.9739	-2.0242

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.14.6
Tokenizers 0.15.0

Downloads last month: 9

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for lewtun/zephyr-7b-dpo-full

Base model

mistralai/Mistral-7B-v0.1

Finetuned

lewtun/zephyr-7b-sft-full

Finetuned

(1)

this model