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

Libraries

How to use LeroyDyer/LCARS_AI_StarTrek_Computer with Transformers:

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

pipe = pipeline("text-generation", model="LeroyDyer/LCARS_AI_StarTrek_Computer")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/LCARS_AI_StarTrek_Computer")
model = AutoModelForCausalLM.from_pretrained("LeroyDyer/LCARS_AI_StarTrek_Computer")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LeroyDyer/LCARS_AI_StarTrek_Computer with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/LeroyDyer/LCARS_AI_StarTrek_Computer

SGLang

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

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

LeroyDyer commited on May 15, 2024

Commit

9d4af4a

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@@ -7,7 +7,12 @@ pipeline_tag: text2text-generation
 tags:
 - nsfw
 ---
 This model is a Collection of merged models via various merge methods :  Reclaiming Previous models which will be orphened by thier parent models :

 tags:
 - nsfw
 ---
+If anybody has star trek data please send as this starship computer database archive needs it!
+then i can correctly theme this model to be inside its role as a starship computer :
+so as well as any space dara ffrom nasa ; i have collected some mufon files which i am still framing the correct prompts for ; for recall as well as interogation :
+I shall also be adding a lot of biblical data and historical data ; from sacred texts; so any generated discussions as phylosophers discussing ancient history and how to solve the problems of the past which they encountered ; in thier lifes: using historical and factual data; as well as playig thier roles after generating a biography and character role to the models to play: they should also be amazed by each others acheivements depending on thier periods:
+we need multiple role and characters for these discussions: as well as as much historical facts and historys as possible to enhance this models abitlity to dicern ancient aliens truth or false : (so we need astrological, astronomical, as well as sizmological and ecological data for the periods of histroy we know : as well as the unfounded suupositions from youtube subtitles !) another useful source of themed data!
 This model is a Collection of merged models via various merge methods :  Reclaiming Previous models which will be orphened by thier parent models :