Instructions to use mohammadhabp/dialogsum_gpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mohammadhabp/dialogsum_gpt2 with Transformers:

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

pipe = pipeline("text-generation", model="mohammadhabp/dialogsum_gpt2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mohammadhabp/dialogsum_gpt2")
model = AutoModelForCausalLM.from_pretrained("mohammadhabp/dialogsum_gpt2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mohammadhabp/dialogsum_gpt2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mohammadhabp/dialogsum_gpt2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mohammadhabp/dialogsum_gpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mohammadhabp/dialogsum_gpt2

SGLang

How to use mohammadhabp/dialogsum_gpt2 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 "mohammadhabp/dialogsum_gpt2" \
    --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": "mohammadhabp/dialogsum_gpt2",
		"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 "mohammadhabp/dialogsum_gpt2" \
        --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": "mohammadhabp/dialogsum_gpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mohammadhabp/dialogsum_gpt2 with Docker Model Runner:
```
docker model run hf.co/mohammadhabp/dialogsum_gpt2
```

dialogsum_gpt2

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

Loss: 2.3211
Rouge1: 0.4866
Rouge2: 0.1609
Rougel: 0.3868

[Repo code]

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel
2.3713	1.0	875	2.3516	0.4821	0.1533	0.3775
2.2024	2.0	1750	2.3249	0.4820	0.1560	0.3806
1.988	3.0	2625	2.3211	0.4866	0.1609	0.3868

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1

Downloads last month: 1

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for mohammadhabp/dialogsum_gpt2

Base model

openai-community/gpt2

Finetuned

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