Instructions to use Zekunli/gpt2-large-NaturalQuestions_1000-ep20 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Zekunli/gpt2-large-NaturalQuestions_1000-ep20 with Transformers:

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

pipe = pipeline("text-generation", model="Zekunli/gpt2-large-NaturalQuestions_1000-ep20")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Zekunli/gpt2-large-NaturalQuestions_1000-ep20")
model = AutoModelForCausalLM.from_pretrained("Zekunli/gpt2-large-NaturalQuestions_1000-ep20", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Zekunli/gpt2-large-NaturalQuestions_1000-ep20 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Zekunli/gpt2-large-NaturalQuestions_1000-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-large-NaturalQuestions_1000-ep20",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Zekunli/gpt2-large-NaturalQuestions_1000-ep20

SGLang

How to use Zekunli/gpt2-large-NaturalQuestions_1000-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-large-NaturalQuestions_1000-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-large-NaturalQuestions_1000-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-large-NaturalQuestions_1000-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-large-NaturalQuestions_1000-ep20",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Zekunli/gpt2-large-NaturalQuestions_1000-ep20 with Docker Model Runner:
```
docker model run hf.co/Zekunli/gpt2-large-NaturalQuestions_1000-ep20
```

gpt2-large-NaturalQuestions_1000-ep20

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

Loss: 1.6582

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: 12
eval_batch_size: 24
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
1.2555	0.6	50	1.0453
0.9434	1.19	100	1.0152
0.5866	1.79	150	1.0386
0.3851	2.38	200	1.1380
0.2864	2.98	250	1.1513
0.1613	3.57	300	1.2101
0.1386	4.17	350	1.2794
0.0886	4.76	400	1.2708
0.0716	5.36	450	1.3450
0.0611	5.95	500	1.3262
0.0353	6.55	550	1.4632
0.0361	7.14	600	1.4107
0.0275	7.74	650	1.4471
0.026	8.33	700	1.4811
0.0177	8.93	750	1.4831
0.015	9.52	800	1.4696
0.0146	10.12	850	1.5135
0.0126	10.71	900	1.5145
0.0118	11.31	950	1.5373
0.0117	11.9	1000	1.5292
0.0141	12.5	1050	1.5793
0.0074	13.1	1100	1.5453
0.0122	13.69	1150	1.5645
0.0092	14.29	1200	1.5721
0.0045	14.88	1250	1.5957
0.0054	15.48	1300	1.6253
0.0048	16.07	1350	1.6197
0.0035	16.67	1400	1.6325
0.0054	17.26	1450	1.6444
0.0041	17.86	1500	1.6465
0.0034	18.45	1550	1.6622
0.0036	19.05	1600	1.6589
0.0035	19.64	1650	1.6578

Framework versions

Transformers 4.29.2
Pytorch 1.10.0+cu111
Datasets 2.5.1
Tokenizers 0.13.3

Downloads last month: 14