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

Libraries

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

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

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

Use Docker

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

SGLang

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

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

zephyr-7b-dpo-full-debug-regression

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

Loss: 0.7240
Rewards/chosen: -4.3843
Rewards/rejected: -7.9101
Rewards/accuracies: 0.7640
Rewards/margins: 3.5258
Logps/rejected: -311.4621
Logps/chosen: -319.5667
Logits/rejected: -2.4790
Logits/chosen: -2.5088

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
total_train_batch_size: 32
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
0.533	0.26	500	0.5084	-0.1902	-1.3680	0.7780	1.1778	-246.0413	-277.6251	-2.9319	-2.9487
0.4907	0.52	1000	0.5234	-0.3346	-1.8153	0.7620	1.4807	-250.5139	-279.0693	-2.8401	-2.8442
0.4388	0.77	1500	0.5202	-0.7856	-2.2720	0.7920	1.4864	-255.0812	-283.5798	-2.7420	-2.7444
0.0651	1.03	2000	0.5049	-1.0044	-2.8702	0.7860	1.8658	-261.0635	-285.7675	-2.7335	-2.7412
0.0887	1.29	2500	0.5946	-1.9888	-3.9256	0.7480	1.9368	-271.6175	-295.6113	-2.5940	-2.6173
0.0747	1.55	3000	0.5748	-1.9590	-4.0271	0.7560	2.0681	-272.6327	-295.3135	-2.4969	-2.5205
0.101	1.81	3500	0.5783	-1.9521	-4.1853	0.7680	2.2332	-274.2144	-295.2442	-2.5069	-2.5278
0.0195	2.07	4000	0.6253	-2.9322	-5.7633	0.7600	2.8310	-289.9938	-305.0455	-2.4935	-2.5158
0.0191	2.32	4500	0.7215	-4.2183	-7.6216	0.7620	3.4034	-308.5774	-317.9060	-2.4756	-2.5036
0.0105	2.58	5000	0.7341	-4.2607	-7.7440	0.7600	3.4833	-309.8016	-318.3306	-2.5156	-2.5437
0.0092	2.84	5500	0.7330	-4.3756	-7.9435	0.7600	3.5679	-311.7966	-319.4794	-2.4856	-2.5149

Framework versions

Transformers 4.35.0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1

Downloads last month: 9

Safetensors

Model size

7B params

Tensor type

BF16

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

Base model

mistralai/Mistral-7B-v0.1

Finetuned

HuggingFaceH4/mistral-7b-sft-beta

Finetuned

(153)

this model