Instructions to use lazyfrog/Report_GPT2-finetuned-financial_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lazyfrog/Report_GPT2-finetuned-financial_data with Transformers:

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

pipe = pipeline("text-generation", model="lazyfrog/Report_GPT2-finetuned-financial_data")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lazyfrog/Report_GPT2-finetuned-financial_data")
model = AutoModelForCausalLM.from_pretrained("lazyfrog/Report_GPT2-finetuned-financial_data", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lazyfrog/Report_GPT2-finetuned-financial_data with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lazyfrog/Report_GPT2-finetuned-financial_data"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lazyfrog/Report_GPT2-finetuned-financial_data",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/lazyfrog/Report_GPT2-finetuned-financial_data

SGLang

How to use lazyfrog/Report_GPT2-finetuned-financial_data 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 "lazyfrog/Report_GPT2-finetuned-financial_data" \
    --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": "lazyfrog/Report_GPT2-finetuned-financial_data",
		"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 "lazyfrog/Report_GPT2-finetuned-financial_data" \
        --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": "lazyfrog/Report_GPT2-finetuned-financial_data",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use lazyfrog/Report_GPT2-finetuned-financial_data with Docker Model Runner:
```
docker model run hf.co/lazyfrog/Report_GPT2-finetuned-financial_data
```

Report_GPT2-finetuned-financial_data

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

Loss: 1.6619

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: 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
1.7034	1.0	3818	1.6879
1.6278	2.0	7636	1.6694
1.596	3.0	11454	1.6619

Framework versions

Transformers 4.26.0
Pytorch 1.12.1+cu113
Datasets 2.9.0
Tokenizers 0.13.2

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