Instructions to use mNLP-project/gpt2-dpo-mcqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mNLP-project/gpt2-dpo-mcqa with Transformers:

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

pipe = pipeline("text-generation", model="mNLP-project/gpt2-dpo-mcqa")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mNLP-project/gpt2-dpo-mcqa")
model = AutoModelForCausalLM.from_pretrained("mNLP-project/gpt2-dpo-mcqa", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mNLP-project/gpt2-dpo-mcqa with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mNLP-project/gpt2-dpo-mcqa"
# 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/gpt2-dpo-mcqa",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mNLP-project/gpt2-dpo-mcqa

SGLang

How to use mNLP-project/gpt2-dpo-mcqa 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/gpt2-dpo-mcqa" \
    --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/gpt2-dpo-mcqa",
		"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/gpt2-dpo-mcqa" \
        --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/gpt2-dpo-mcqa",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mNLP-project/gpt2-dpo-mcqa with Docker Model Runner:
```
docker model run hf.co/mNLP-project/gpt2-dpo-mcqa
```

gpt2-dpo-mcqa

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

Loss: 0.6310
Rewards/chosen: 1.4580
Rewards/rejected: 1.1845
Rewards/accuracies: 0.6414
Rewards/margins: 0.2735
Logps/rejected: -659.0944
Logps/chosen: -787.4795
Logits/rejected: -14.9328
Logits/chosen: -11.6364

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: 1e-07
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.2
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.6407	0.9993	668	0.6460	0.7721	0.6216	0.6295	0.1505	-664.7236	-794.3383	-15.1273	-11.7899
0.6498	2.0	1337	0.6374	1.2927	1.0475	0.6325	0.2453	-660.4651	-789.1318	-14.9517	-11.6401
0.6468	2.9993	2005	0.6342	1.3734	1.1102	0.6388	0.2632	-659.8373	-788.3249	-14.9535	-11.6481
0.6113	4.0	2674	0.6332	1.3317	1.0769	0.6444	0.2548	-660.1705	-788.7426	-14.9930	-11.6897
0.5826	4.9993	3342	0.6310	1.4580	1.1845	0.6414	0.2735	-659.0944	-787.4795	-14.9328	-11.6364
0.5613	6.0	4011	0.6317	1.4979	1.2181	0.6407	0.2798	-658.7584	-787.0804	-14.9234	-11.6271
0.581	6.9993	4679	0.6316	1.5084	1.2260	0.6437	0.2825	-658.6798	-786.9750	-14.9319	-11.6377
0.571	8.0	5348	0.6320	1.4992	1.2184	0.6425	0.2808	-658.7557	-787.0676	-14.9334	-11.6373
0.5943	8.9993	6016	0.6317	1.5126	1.2294	0.6437	0.2832	-658.6454	-786.9331	-14.9226	-11.6269
0.5635	9.9925	6680	0.6317	1.5142	1.2308	0.6433	0.2835	-658.6317	-786.9168	-14.9211	-11.6256

Framework versions

Transformers 4.40.2
Pytorch 2.1.0+cu118
Datasets 2.19.1
Tokenizers 0.19.1

Downloads last month: 8

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for mNLP-project/gpt2-dpo-mcqa

Base model

openai-community/gpt2

Finetuned

mNLP-project/gpt2-finetuned-mcqa

Finetuned

(1)

this model