Instructions to use Geo/gpt2_custom_c_q_and_a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Geo/gpt2_custom_c_q_and_a with Transformers:

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

pipe = pipeline("text-generation", model="Geo/gpt2_custom_c_q_and_a")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Geo/gpt2_custom_c_q_and_a")
model = AutoModelForCausalLM.from_pretrained("Geo/gpt2_custom_c_q_and_a")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Geo/gpt2_custom_c_q_and_a with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Geo/gpt2_custom_c_q_and_a"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Geo/gpt2_custom_c_q_and_a",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Geo/gpt2_custom_c_q_and_a

SGLang

How to use Geo/gpt2_custom_c_q_and_a 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 "Geo/gpt2_custom_c_q_and_a" \
    --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": "Geo/gpt2_custom_c_q_and_a",
		"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 "Geo/gpt2_custom_c_q_and_a" \
        --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": "Geo/gpt2_custom_c_q_and_a",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Geo/gpt2_custom_c_q_and_a with Docker Model Runner:
```
docker model run hf.co/Geo/gpt2_custom_c_q_and_a
```

gpt2_custom_c_q_and_a

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

Loss: 0.0098

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: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 80

Training results

Training Loss	Epoch	Step	Validation Loss
2.5542	4.76	100	0.7706
0.4813	9.52	200	0.1839
0.1838	14.29	300	0.0892
0.109	19.05	400	0.0532
0.0767	23.81	500	0.0385
0.0579	28.57	600	0.0290
0.0447	33.33	700	0.0213
0.0365	38.1	800	0.0178
0.0319	42.86	900	0.0157
0.0277	47.62	1000	0.0140
0.0252	52.38	1100	0.0125
0.0229	57.14	1200	0.0113
0.0213	61.9	1300	0.0107
0.0199	66.67	1400	0.0104
0.0181	71.43	1500	0.0099
0.0184	76.19	1600	0.0098

Framework versions

Transformers 4.32.1
Pytorch 2.0.1+cu118
Tokenizers 0.13.3

Downloads last month: 5

Model tree for Geo/gpt2_custom_c_q_and_a

Base model

lighteternal/gpt2-finetuned-greek

Finetuned

(4)

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