Instructions to use jarod0411/stage2Only_scaffoldGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jarod0411/stage2Only_scaffoldGPT with Transformers:

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

pipe = pipeline("text-generation", model="jarod0411/stage2Only_scaffoldGPT")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("jarod0411/stage2Only_scaffoldGPT")
model = AutoModelForMultimodalLM.from_pretrained("jarod0411/stage2Only_scaffoldGPT")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jarod0411/stage2Only_scaffoldGPT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jarod0411/stage2Only_scaffoldGPT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jarod0411/stage2Only_scaffoldGPT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jarod0411/stage2Only_scaffoldGPT

SGLang

How to use jarod0411/stage2Only_scaffoldGPT 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 "jarod0411/stage2Only_scaffoldGPT" \
    --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": "jarod0411/stage2Only_scaffoldGPT",
		"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 "jarod0411/stage2Only_scaffoldGPT" \
        --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": "jarod0411/stage2Only_scaffoldGPT",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jarod0411/stage2Only_scaffoldGPT with Docker Model Runner:
```
docker model run hf.co/jarod0411/stage2Only_scaffoldGPT
```

stage2Only_scaffoldGPT

This model is a fine-tuned version of gpt2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.3794
Accuracy: 0.8768

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: 24
eval_batch_size: 24
seed: 42
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 192
total_eval_batch_size: 192
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.4672	1.0	6015	0.4400	0.8613
0.4321	2.0	12030	0.4123	0.8684
0.417	3.0	18045	0.4000	0.8716
0.4082	4.0	24060	0.3928	0.8734
0.4024	5.0	30075	0.3881	0.8746
0.3982	6.0	36090	0.3847	0.8754
0.3953	7.0	42105	0.3824	0.8760
0.3932	8.0	48120	0.3807	0.8765
0.3919	9.0	54135	0.3798	0.8767
0.3912	10.0	60150	0.3794	0.8768

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.1.1+cu121
Datasets 2.15.0
Tokenizers 0.15.0

Downloads last month: 5

Safetensors

Model size

0.2B params

Tensor type

F32

Model tree for jarod0411/stage2Only_scaffoldGPT

Base model

openai-community/gpt2

Finetuned

(2182)

this model