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

Libraries

How to use KaixuanJi/zephyr-7b-dpo-lora with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KaixuanJi/zephyr-7b-dpo-lora")
model = AutoModelForCausalLM.from_pretrained("KaixuanJi/zephyr-7b-dpo-lora", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KaixuanJi/zephyr-7b-dpo-lora with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/KaixuanJi/zephyr-7b-dpo-lora

SGLang

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

Docker Model Runner
How to use KaixuanJi/zephyr-7b-dpo-lora with Docker Model Runner:
```
docker model run hf.co/KaixuanJi/zephyr-7b-dpo-lora
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

zephyr-7b-dpo-lora

This model is a fine-tuned version of HuggingFaceH4/zephyr-7b-beta on the None dataset. It achieves the following results on the evaluation set:

eval_loss: 0.6931
eval_runtime: 817.0722
eval_samples_per_second: 2.448
eval_steps_per_second: 0.153
eval_rewards/chosen: 0.0
eval_rewards/rejected: 0.0
eval_rewards/accuracies: 0.0
eval_rewards/margins: 0.0
eval_logps/rejected: -311.3714
eval_logps/chosen: -319.0738
eval_logits/rejected: -2.3541
eval_logits/chosen: -2.4051
step: 0

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: 4
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 64
total_eval_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 0

Framework versions

Transformers 4.35.0
Pytorch 2.1.1+cu121
Datasets 2.14.6
Tokenizers 0.14.1

Downloads last month: 38

Model tree for KaixuanJi/zephyr-7b-dpo-lora

Base model

mistralai/Mistral-7B-v0.1

Finetuned

HuggingFaceH4/zephyr-7b-beta

Finetuned

(147)

this model

Quantizations

1 model