Instructions to use bencyc1129/mitre-gpt2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bencyc1129/mitre-gpt2-base with Transformers:

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

pipe = pipeline("text-generation", model="bencyc1129/mitre-gpt2-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bencyc1129/mitre-gpt2-base")
model = AutoModelForCausalLM.from_pretrained("bencyc1129/mitre-gpt2-base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use bencyc1129/mitre-gpt2-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bencyc1129/mitre-gpt2-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bencyc1129/mitre-gpt2-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bencyc1129/mitre-gpt2-base

SGLang

How to use bencyc1129/mitre-gpt2-base 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 "bencyc1129/mitre-gpt2-base" \
    --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": "bencyc1129/mitre-gpt2-base",
		"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 "bencyc1129/mitre-gpt2-base" \
        --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": "bencyc1129/mitre-gpt2-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bencyc1129/mitre-gpt2-base with Docker Model Runner:
```
docker model run hf.co/bencyc1129/mitre-gpt2-base
```

mitre-gpt2-base

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

Loss: 3.3404

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss
3.2933	2.72	1000	2.6028
2.2619	5.45	2000	2.4654
1.9152	8.17	3000	2.3952
1.6434	10.9	4000	2.3729
1.4289	13.62	5000	2.4208
1.2627	16.35	6000	2.4845
1.1301	19.07	7000	2.5619
1.0169	21.8	8000	2.6058
0.93	24.52	9000	2.6773
0.8587	27.25	10000	2.7389
0.8032	29.97	11000	2.7639
0.7506	32.7	12000	2.8329
0.7079	35.42	13000	2.8934
0.6781	38.15	14000	2.9175
0.6461	40.87	15000	2.9532
0.6205	43.6	16000	3.0008
0.5987	46.32	17000	3.0539
0.5811	49.05	18000	3.0738
0.564	51.77	19000	3.0972
0.5491	54.5	20000	3.1341
0.5377	57.22	21000	3.1558
0.5255	59.95	22000	3.1723
0.516	62.67	23000	3.1984
0.5077	65.4	24000	3.2163
0.5021	68.12	25000	3.2396
0.4946	70.84	26000	3.2413
0.4871	73.57	27000	3.2708
0.4845	76.29	28000	3.2833
0.4791	79.02	29000	3.2847
0.4739	81.74	30000	3.2950
0.4704	84.47	31000	3.3124
0.4678	87.19	32000	3.3122
0.4642	89.92	33000	3.3260
0.4617	92.64	34000	3.3326
0.4605	95.37	35000	3.3325
0.4576	98.09	36000	3.3404

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Downloads last month: 12

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for bencyc1129/mitre-gpt2-base

Base model

openai-community/gpt2

Finetuned

(2154)

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