Instructions to use currentlyexhausted/mobile-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use currentlyexhausted/mobile-llm with Transformers:

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

pipe = pipeline("text-generation", model="currentlyexhausted/mobile-llm")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("currentlyexhausted/mobile-llm")
model = AutoModelForSeq2SeqLM.from_pretrained("currentlyexhausted/mobile-llm", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use currentlyexhausted/mobile-llm with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/currentlyexhausted/mobile-llm

SGLang

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

Docker Model Runner
How to use currentlyexhausted/mobile-llm with Docker Model Runner:
```
docker model run hf.co/currentlyexhausted/mobile-llm
```

mobile-llm / README.md

currentlyexhausted

Create README.md

49ecc10 over 3 years ago

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metadata

license: mit
datasets:
  - yahma/alpaca-cleaned
language:
  - en
metrics:
  - bertscore
library_name: transformers
pipeline_tag: text2text-generation
tags:
  - t5
  - pocket
  - llm
  - mobile
  - alpaca
  - flan-t5