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 Oct 22, 2024

Commit

0eaabed

verified ·

1 Parent(s): 9d4af4a

Update README.md

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README.md +13 -10

README.md CHANGED Viewed

@@ -5,7 +5,18 @@ language:
 library_name: transformers
 pipeline_tag: text2text-generation
 tags:
-- nsfw
 ---
 If anybody has star trek data please send as this starship computer database archive needs it!
@@ -23,12 +34,4 @@ So those models were merged into other models which had been specifically traine
 the models were heavliy dpo trained ; and various newer methodologies installed : the deep mind series is a special series which contains self correction recal, visio spacial ... step by step thinking:
 SO the multi merge often fizes these errors between models as well as training gaps :Hopefully they all took and merged well !
-Performing even unknown and unprogrammed tasks:

 library_name: transformers
 pipeline_tag: text2text-generation
 tags:
+- LCARS
+- Star-Trek
+- 128k-Context
+- mistral
+- chemistry
+- biology
+- finance
+- legal
+- art
+- code
+- medical
+- text-generation-inference
 ---
 If anybody has star trek data please send as this starship computer database archive needs it!
 the models were heavliy dpo trained ; and various newer methodologies installed : the deep mind series is a special series which contains self correction recal, visio spacial ... step by step thinking:
 SO the multi merge often fizes these errors between models as well as training gaps :Hopefully they all took and merged well !
+Performing even unknown and unprogrammed tasks: