- Libraries
- Transformers
How to use efficiencyx/Jun-LoRA-E2B-LiteRT with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="efficiencyx/Jun-LoRA-E2B-LiteRT") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("efficiencyx/Jun-LoRA-E2B-LiteRT", device_map="auto") - LiteRT
How to use efficiencyx/Jun-LoRA-E2B-LiteRT with LiteRT:
# No code snippets available yet for this library.
# To use this model, check the repository files and the library's documentation.
# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js
- LiteRT-LM
How to use efficiencyx/Jun-LoRA-E2B-LiteRT with LiteRT-LM:
# LiteRT-LM runs on various platforms (Android, iOS, Windows, Linux, macOS, IoT, Web/WASM)
# and supports many APIs (C++, Python, Kotlin, Swift, JavaScript, Flutter).
# For platform-specific integration guides, please refer to the official developer website:
# https://ai.google.dev/edge/litert-lm
# To try LiteRT-LM, the easiest way is to use our CLI tool.
# 1. Install the LiteRT-LM CLI tool:
pip install -U litert-lm
# 2. Download and run this model locally:
# See: https://ai.google.dev/edge/litert-lm/cli
# A single .litertlm file in the repo is picked automatically; otherwise the CLI asks which one to run
# (or pass its name right after the repo id).
litert-lm run \
--from-huggingface-repo=efficiencyx/Jun-LoRA-E2B-LiteRT \
--prompt="Write me a poem"
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use efficiencyx/Jun-LoRA-E2B-LiteRT with vLLM:
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "efficiencyx/Jun-LoRA-E2B-LiteRT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "efficiencyx/Jun-LoRA-E2B-LiteRT",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'Use Docker
docker model run hf.co/efficiencyx/Jun-LoRA-E2B-LiteRT
- SGLang
How to use efficiencyx/Jun-LoRA-E2B-LiteRT 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 "efficiencyx/Jun-LoRA-E2B-LiteRT" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "efficiencyx/Jun-LoRA-E2B-LiteRT",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'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 "efficiencyx/Jun-LoRA-E2B-LiteRT" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "efficiencyx/Jun-LoRA-E2B-LiteRT",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}' - Docker Model Runner
How to use efficiencyx/Jun-LoRA-E2B-LiteRT with Docker Model Runner:
docker model run hf.co/efficiencyx/Jun-LoRA-E2B-LiteRT
