Instructions to use NanQiangHF/gpt2_bwgenerator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NanQiangHF/gpt2_bwgenerator with Transformers:

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

pipe = pipeline("text-generation", model="NanQiangHF/gpt2_bwgenerator")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NanQiangHF/gpt2_bwgenerator")
model = AutoModelForCausalLM.from_pretrained("NanQiangHF/gpt2_bwgenerator", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NanQiangHF/gpt2_bwgenerator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NanQiangHF/gpt2_bwgenerator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NanQiangHF/gpt2_bwgenerator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NanQiangHF/gpt2_bwgenerator

SGLang

How to use NanQiangHF/gpt2_bwgenerator 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 "NanQiangHF/gpt2_bwgenerator" \
    --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": "NanQiangHF/gpt2_bwgenerator",
		"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 "NanQiangHF/gpt2_bwgenerator" \
        --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": "NanQiangHF/gpt2_bwgenerator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NanQiangHF/gpt2_bwgenerator with Docker Model Runner:
```
docker model run hf.co/NanQiangHF/gpt2_bwgenerator
```

gpt2_bwgenerator

This model is a fine-tuned version of openai-community/gpt2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0925

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: 32
eval_batch_size: 32
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
0.3023	0.1574	300	0.1644
0.167	0.3148	600	0.1376
0.1466	0.4722	900	0.1301
0.1354	0.6296	1200	0.1180
0.1258	0.7870	1500	0.1129
0.1198	0.9444	1800	0.1061
0.115	1.1018	2100	0.1030
0.1108	1.2592	2400	0.1013
0.1088	1.4166	2700	0.1000
0.1067	1.5740	3000	0.0982
0.1049	1.7314	3300	0.0974
0.1039	1.8888	3600	0.0960
0.1024	2.0462	3900	0.0952
0.1013	2.2036	4200	0.0947
0.1006	2.3610	4500	0.0944
0.0997	2.5184	4800	0.0935
0.0993	2.6758	5100	0.0927
0.099	2.8332	5400	0.0928
0.0983	2.9906	5700	0.0925

Framework versions

Transformers 4.47.1
Pytorch 2.5.1
Datasets 3.2.0
Tokenizers 0.21.0

Downloads last month: 6

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for NanQiangHF/gpt2_bwgenerator

Base model

openai-community/gpt2

Finetuned

(2255)

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