Instructions to use progs2002/star-trek-tng-script-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use progs2002/star-trek-tng-script-generator with Transformers:

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

pipe = pipeline("text-generation", model="progs2002/star-trek-tng-script-generator")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("progs2002/star-trek-tng-script-generator")
model = AutoModelForCausalLM.from_pretrained("progs2002/star-trek-tng-script-generator", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use progs2002/star-trek-tng-script-generator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "progs2002/star-trek-tng-script-generator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "progs2002/star-trek-tng-script-generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/progs2002/star-trek-tng-script-generator

SGLang

How to use progs2002/star-trek-tng-script-generator 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 "progs2002/star-trek-tng-script-generator" \
    --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": "progs2002/star-trek-tng-script-generator",
		"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 "progs2002/star-trek-tng-script-generator" \
        --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": "progs2002/star-trek-tng-script-generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use progs2002/star-trek-tng-script-generator with Docker Model Runner:
```
docker model run hf.co/progs2002/star-trek-tng-script-generator
```

data cleaning and training code

https://github.com/progs2002/StarTrekTNG-ScriptGenerator

star-trek-tng-script-generator

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

Loss: 2.8459

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: 0.0002
train_batch_size: 1
eval_batch_size: 1
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 50
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
3.1502	0.13	500	3.0233
3.0538	0.26	1000	2.9728
2.9951	0.38	1500	2.9437
2.9891	0.51	2000	2.9125
2.9289	0.64	2500	2.9159
2.9091	0.77	3000	2.9008
2.8916	0.89	3500	2.8752
2.8122	1.02	4000	2.8881
2.5224	1.15	4500	2.8896
2.5284	1.28	5000	2.8667
2.5191	1.4	5500	2.8599
2.5119	1.53	6000	2.8488
2.4808	1.66	6500	2.8296
2.4601	1.79	7000	2.8081
2.4331	1.91	7500	2.7993
2.3716	2.04	8000	2.8518
2.1528	2.17	8500	2.8634
2.1276	2.3	9000	2.8617
2.1329	2.43	9500	2.8489
2.1135	2.55	10000	2.8446
2.1259	2.68	10500	2.8461
2.1142	2.81	11000	2.8472
2.1071	2.94	11500	2.8459

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0

Downloads last month: 5

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for progs2002/star-trek-tng-script-generator

Base model

openai-community/gpt2

Finetuned

(2261)

this model

progs2002
/

star-trek-tng-script-generator

data cleaning and training code

star-trek-tng-script-generator

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for progs2002/star-trek-tng-script-generator

Dataset used to train progs2002/star-trek-tng-script-generator

Spaces using progs2002/star-trek-tng-script-generator 2