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

Libraries

How to use ptsv/tinystories_baseline_full with Transformers:

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

pipe = pipeline("text-generation", model="ptsv/tinystories_baseline_full")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("ptsv/tinystories_baseline_full")
model = AutoModelForMultimodalLM.from_pretrained("ptsv/tinystories_baseline_full")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ptsv/tinystories_baseline_full with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ptsv/tinystories_baseline_full

SGLang

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

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

tinystories_baseline_full

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

Loss: 1.3218

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: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
1.4412	0.6196	400	1.4200
1.3608	1.2391	800	1.3685
1.3188	1.8587	1200	1.3417
1.3296	2.4782	1600	1.3260

Framework versions

Transformers 4.44.1
Pytorch 2.2.2
Datasets 2.18.0
Tokenizers 0.19.1

Downloads last month: 1

Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for ptsv/tinystories_baseline_full

Base model

openai-community/gpt2-medium

Finetuned

(193)

this model