Instructions to use dlibf/zephyr-7b-dpo-full_lr5e-6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dlibf/zephyr-7b-dpo-full_lr5e-6 with Transformers:

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

pipe = pipeline("text-generation", model="dlibf/zephyr-7b-dpo-full_lr5e-6")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dlibf/zephyr-7b-dpo-full_lr5e-6")
model = AutoModelForCausalLM.from_pretrained("dlibf/zephyr-7b-dpo-full_lr5e-6")
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 dlibf/zephyr-7b-dpo-full_lr5e-6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dlibf/zephyr-7b-dpo-full_lr5e-6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dlibf/zephyr-7b-dpo-full_lr5e-6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dlibf/zephyr-7b-dpo-full_lr5e-6

SGLang

How to use dlibf/zephyr-7b-dpo-full_lr5e-6 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 "dlibf/zephyr-7b-dpo-full_lr5e-6" \
    --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": "dlibf/zephyr-7b-dpo-full_lr5e-6",
		"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 "dlibf/zephyr-7b-dpo-full_lr5e-6" \
        --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": "dlibf/zephyr-7b-dpo-full_lr5e-6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dlibf/zephyr-7b-dpo-full_lr5e-6 with Docker Model Runner:
```
docker model run hf.co/dlibf/zephyr-7b-dpo-full_lr5e-6
```

zephyr-7b-dpo-full_lr5e-6

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

Loss: 0.4945
Rewards/chosen: -2.8495
Rewards/rejected: -3.9547
Rewards/accuracies: 0.7695
Rewards/margins: 1.1052
Logps/rejected: -657.4872
Logps/chosen: -547.3602
Logits/rejected: 4.2623
Logits/chosen: 3.2552

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-06
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	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.605	0.21	100	0.5799	-1.9111	-2.5875	0.7461	0.6764	-520.7654	-453.5230	-2.4063	-2.5964
0.5303	0.42	200	0.5347	-2.5277	-3.3091	0.7656	0.7814	-592.9240	-515.1846	2.6876	1.5286
0.5147	0.63	300	0.5106	-2.4842	-3.4691	0.75	0.9850	-608.9282	-510.8301	2.5319	1.2911
0.498	0.84	400	0.4958	-2.9252	-4.0433	0.7734	1.1181	-666.3457	-554.9301	4.6654	3.6821

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.14.6
Tokenizers 0.15.1

Downloads last month: 3

Safetensors

Model size

7B params

Tensor type

BF16