Instructions to use mNLP-project/distilgpt2-dpo_test_run with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mNLP-project/distilgpt2-dpo_test_run with Transformers:

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

pipe = pipeline("text-generation", model="mNLP-project/distilgpt2-dpo_test_run")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mNLP-project/distilgpt2-dpo_test_run")
model = AutoModelForCausalLM.from_pretrained("mNLP-project/distilgpt2-dpo_test_run")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mNLP-project/distilgpt2-dpo_test_run with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mNLP-project/distilgpt2-dpo_test_run"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mNLP-project/distilgpt2-dpo_test_run",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mNLP-project/distilgpt2-dpo_test_run

SGLang

How to use mNLP-project/distilgpt2-dpo_test_run 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 "mNLP-project/distilgpt2-dpo_test_run" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mNLP-project/distilgpt2-dpo_test_run",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "mNLP-project/distilgpt2-dpo_test_run" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mNLP-project/distilgpt2-dpo_test_run",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mNLP-project/distilgpt2-dpo_test_run with Docker Model Runner:
```
docker model run hf.co/mNLP-project/distilgpt2-dpo_test_run
```

distilgpt2-dpo_test_run

This model is a fine-tuned version of gpt2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.9044
Rewards/chosen: 0.7444
Rewards/rejected: 0.2592
Rewards/accuracies: 0.5817
Rewards/margins: 0.4852
Logps/rejected: -429.5133
Logps/chosen: -506.8889
Logits/rejected: -50.2012
Logits/chosen: -45.4443

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-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_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: 6

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.8683	1.0	1337	0.9044	0.7444	0.2592	0.5817	0.4852	-429.5133	-506.8889	-50.2012	-45.4443
0.4795	2.0	2674	0.9425	0.1993	-0.4639	0.5959	0.6632	-436.7442	-512.3394	-54.4344	-49.5827
0.1485	3.0	4011	1.1159	-2.0134	-2.6798	0.5775	0.6664	-458.9030	-534.4666	-70.3363	-65.4014
0.0378	4.0	5348	1.3151	-3.6174	-4.7588	0.5927	1.1415	-479.6934	-550.5060	-70.8835	-65.6636
0.0127	5.0	6685	1.4381	-4.8640	-6.0585	0.5822	1.1945	-492.6903	-562.9730	-70.3612	-64.6966
0.0006	6.0	8022	1.5074	-5.3161	-6.4742	0.5837	1.1581	-496.8472	-567.4940	-70.7820	-64.9708

Framework versions

Transformers 4.40.2
Pytorch 2.1.0+cu118
Datasets 2.19.1
Tokenizers 0.19.1

Downloads last month: 6

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for mNLP-project/distilgpt2-dpo_test_run

Base model

openai-community/gpt2

Finetuned

(2159)

this model