Instructions to use Zekunli/gpt2-NaturalQuestions_4000-ep20 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Zekunli/gpt2-NaturalQuestions_4000-ep20 with Transformers:

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

pipe = pipeline("text-generation", model="Zekunli/gpt2-NaturalQuestions_4000-ep20")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Zekunli/gpt2-NaturalQuestions_4000-ep20")
model = AutoModelForCausalLM.from_pretrained("Zekunli/gpt2-NaturalQuestions_4000-ep20")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Zekunli/gpt2-NaturalQuestions_4000-ep20 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Zekunli/gpt2-NaturalQuestions_4000-ep20"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Zekunli/gpt2-NaturalQuestions_4000-ep20",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Zekunli/gpt2-NaturalQuestions_4000-ep20

SGLang

How to use Zekunli/gpt2-NaturalQuestions_4000-ep20 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 "Zekunli/gpt2-NaturalQuestions_4000-ep20" \
    --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": "Zekunli/gpt2-NaturalQuestions_4000-ep20",
		"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 "Zekunli/gpt2-NaturalQuestions_4000-ep20" \
        --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": "Zekunli/gpt2-NaturalQuestions_4000-ep20",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Zekunli/gpt2-NaturalQuestions_4000-ep20 with Docker Model Runner:
```
docker model run hf.co/Zekunli/gpt2-NaturalQuestions_4000-ep20
```

gpt2-NaturalQuestions_4000-ep20

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

Loss: 1.2717

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: 2e-05
train_batch_size: 48
eval_batch_size: 96
seed: 1799
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss
1.7832	0.6	50	1.3857
1.4409	1.19	100	1.3070
1.312	1.79	150	1.2716
1.2412	2.38	200	1.2514
1.1902	2.98	250	1.2283
1.1171	3.57	300	1.2307
1.0728	4.17	350	1.2228
1.022	4.76	400	1.2145
1.0001	5.36	450	1.2225
0.9645	5.95	500	1.2183
0.9175	6.55	550	1.2185
0.911	7.14	600	1.2179
0.877	7.74	650	1.2218
0.8365	8.33	700	1.2260
0.8387	8.93	750	1.2223
0.8047	9.52	800	1.2289
0.7822	10.12	850	1.2304
0.7652	10.71	900	1.2353
0.7502	11.31	950	1.2370
0.7275	11.9	1000	1.2411
0.6998	12.5	1050	1.2515
0.7128	13.1	1100	1.2465
0.6865	13.69	1150	1.2553
0.6748	14.29	1200	1.2544
0.6661	14.88	1250	1.2563
0.6636	15.48	1300	1.2592
0.6403	16.07	1350	1.2630
0.6309	16.67	1400	1.2679
0.6281	17.26	1450	1.2667
0.6237	17.86	1500	1.2692
0.621	18.45	1550	1.2708
0.6195	19.05	1600	1.2711
0.6123	19.64	1650	1.2713

Framework versions

Transformers 4.29.2
Pytorch 1.10.0+cu111
Datasets 2.5.1
Tokenizers 0.13.3

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