Instructions to use Bainbridge/gpt2-kl_1_07-hs_cn_decay with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bainbridge/gpt2-kl_1_07-hs_cn_decay with Transformers:

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

pipe = pipeline("text-generation", model="Bainbridge/gpt2-kl_1_07-hs_cn_decay")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Bainbridge/gpt2-kl_1_07-hs_cn_decay")
model = AutoModelForCausalLM.from_pretrained("Bainbridge/gpt2-kl_1_07-hs_cn_decay", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Bainbridge/gpt2-kl_1_07-hs_cn_decay with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Bainbridge/gpt2-kl_1_07-hs_cn_decay"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bainbridge/gpt2-kl_1_07-hs_cn_decay",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Bainbridge/gpt2-kl_1_07-hs_cn_decay

SGLang

How to use Bainbridge/gpt2-kl_1_07-hs_cn_decay 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 "Bainbridge/gpt2-kl_1_07-hs_cn_decay" \
    --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": "Bainbridge/gpt2-kl_1_07-hs_cn_decay",
		"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 "Bainbridge/gpt2-kl_1_07-hs_cn_decay" \
        --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": "Bainbridge/gpt2-kl_1_07-hs_cn_decay",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Bainbridge/gpt2-kl_1_07-hs_cn_decay with Docker Model Runner:
```
docker model run hf.co/Bainbridge/gpt2-kl_1_07-hs_cn_decay
```

gpt2-kl_1_07-hs_cn_decay

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

Loss: 0.5387

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: 4
seed: 21
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
74.3927	0.02	10	69.5741
46.8756	0.04	20	32.9634
14.3637	0.06	30	10.6462
7.5685	0.08	40	4.2441
4.4164	0.1	50	2.0652
2.2355	0.12	60	1.1276
1.7411	0.14	70	0.8881
1.5599	0.16	80	0.7747
1.5609	0.18	90	0.6453
1.4701	0.2	100	0.6145
1.3214	0.22	110	0.6367
1.3795	0.24	120	0.6098
1.3714	0.26	130	0.5903
1.5096	0.28	140	0.5764
1.4983	0.3	150	0.5695
1.3707	0.32	160	0.5712
1.2352	0.34	170	0.5689
1.4272	0.36	180	0.5650
1.4578	0.38	190	0.5599
1.2548	0.4	200	0.5577
1.488	0.42	210	0.5575
1.3988	0.44	220	0.5518
1.1963	0.46	230	0.5536
1.2992	0.48	240	0.5519
1.1852	0.5	250	0.5517
1.2691	0.52	260	0.5473
1.2183	0.54	270	0.5433
1.3085	0.56	280	0.5443
1.1756	0.58	290	0.5422
1.1152	0.6	300	0.5420
1.2707	0.62	310	0.5391
1.3634	0.64	320	0.5376
1.1368	0.66	330	0.5378
1.1393	0.68	340	0.5378
1.1335	0.7	350	0.5387

Framework versions

Transformers 4.28.0
Pytorch 1.11.0+cu113
Datasets 2.11.0
Tokenizers 0.12.1

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