Instructions to use tianlinliu0121/zephyr-7b-dpo-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tianlinliu0121/zephyr-7b-dpo-full with Transformers:

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

pipe = pipeline("text-generation", model="tianlinliu0121/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("tianlinliu0121/zephyr-7b-dpo-full")
model = AutoModelForCausalLM.from_pretrained("tianlinliu0121/zephyr-7b-dpo-full", 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 tianlinliu0121/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 "tianlinliu0121/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": "tianlinliu0121/zephyr-7b-dpo-full",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tianlinliu0121/zephyr-7b-dpo-full

SGLang

How to use tianlinliu0121/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 "tianlinliu0121/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": "tianlinliu0121/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 "tianlinliu0121/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": "tianlinliu0121/zephyr-7b-dpo-full",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tianlinliu0121/zephyr-7b-dpo-full with Docker Model Runner:
```
docker model run hf.co/tianlinliu0121/zephyr-7b-dpo-full
```

zephyr-7b-dpo-full

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

Loss: 0.6641
Rewards/chosen: -4.2025
Rewards/rejected: -7.5064
Rewards/accuracies: 0.7740
Rewards/margins: 3.3038
Logps/rejected: -318.9220
Logps/chosen: -321.7840
Logits/rejected: -2.6752
Logits/chosen: -2.7102

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: 4
total_train_batch_size: 128
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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.5058	0.21	100	0.4911	-0.1160	-1.1492	0.7620	1.0332	-242.3301	-272.5491	-3.0425	-3.0547
0.4946	0.41	200	0.4825	-0.3097	-1.5239	0.7660	1.2143	-246.8446	-274.8822	-2.9173	-2.9312
0.4777	0.62	300	0.4710	-0.1682	-1.3949	0.7800	1.2266	-245.2894	-273.1778	-2.9493	-2.9706
0.4795	0.83	400	0.4613	-0.3814	-1.7739	0.7880	1.3925	-249.8568	-275.7468	-2.8663	-2.8864
0.1195	1.03	500	0.4680	-0.6715	-2.3344	0.7880	1.6629	-256.6091	-279.2414	-2.7880	-2.8173
0.1038	1.24	600	0.4890	-1.2315	-3.1990	0.7740	1.9675	-267.0262	-285.9885	-2.7944	-2.8235
0.1091	1.45	700	0.4969	-1.5189	-3.4689	0.7720	1.9499	-270.2775	-289.4514	-2.7692	-2.7947
0.1017	1.65	800	0.4938	-1.5010	-3.5680	0.7780	2.0670	-271.4719	-289.2352	-2.7970	-2.8233
0.0853	1.86	900	0.5150	-1.9917	-4.1892	0.7840	2.1975	-278.9565	-295.1473	-2.7667	-2.7959
0.0214	2.07	1000	0.5713	-2.9307	-5.6016	0.7740	2.6709	-295.9729	-306.4606	-2.7489	-2.7811
0.0169	2.27	1100	0.6226	-3.6197	-6.6310	0.7760	3.0112	-308.3753	-314.7625	-2.7096	-2.7449
0.0192	2.48	1200	0.6315	-3.8462	-6.9302	0.7760	3.0839	-311.9800	-317.4913	-2.6779	-2.7147
0.0157	2.68	1300	0.6585	-4.1276	-7.3886	0.7760	3.2610	-317.5031	-320.8812	-2.6830	-2.7171
0.018	2.89	1400	0.6696	-4.2900	-7.6252	0.7740	3.3352	-320.3536	-322.8378	-2.6703	-2.7057

Framework versions

Transformers 4.35.0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1

Downloads last month: 7

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for tianlinliu0121/zephyr-7b-dpo-full

Base model

mistralai/Mistral-7B-v0.1

Finetuned

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

Finetuned

(393)

this model