min-llm-server-client 0.3.5

A minimal Flask API server for local HuggingFace LLMs

LLM REST API

The simplest possible Python code for running local LLM inference as a REST API server (with a simple client).

This package lets you start an inference server for Hugging Face–compatible models (like LLaMA, Qwen, GPT-OSS, etc.) on your own computer or server, and make it accessible to applications via HTTP.

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Installation

From PyPI (recommended):

pip install min-llm-server-client

From source:

git clone https://github.com/afshinsadeghi/min_llm_server_client.git
cd min_llm_server_client
pip install .

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Usage

Starting the Server

After installation, you can launch the server with the provided CLI entrypoint:

min-llm-server --model_name meta-llama/Llama-3.3-70B-Instruct --max_new_tokens 100 --device cuda:0

Options:

• --model_name : Hugging Face model name or local path (e.g. openai/gpt-oss-20b, meta-llama/Llama-3.3-70B-Instruct, or /path/to/model).
• --max_new_tokens : maximum number of tokens to generate in response.
• --device : cpu, cuda:0, cuda:1, etc.

Example (CPU run):

min-llm-server --model_name openai/gpt-oss-20b --max_new_tokens 50 --device cpu

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Sending Queries

Once the server is running (default: http://127.0.0.1:5000/llm/q), you can query it with curl or Python.

Curl:

curl -X POST http://127.0.0.1:5000/llm/q \
  -H "Content-Type: application/json" \
  -d '{"query": "What is Earth?", "key": "key1"}'

Python client:

from min_llm_server_client.local_llm_inference_api_client import send_query

response = send_query("What is the capital of France?", user="user1", key="key1")
print(response)

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Performance notes

• Running LLaMA 3.1 8B:
  • Intel CPU → ~30 seconds per request, ~2.4 GB RAM
  • A100 GPU → <1 second per request, ~34 GB GPU memory, ~4.8 GB CPU RAM

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Project Structure

min_llm_server_client/
├── src/
│   ├── local_llm_inference_api_client.py
│   ├── local_llm_inference_server_api.py
│   └── ...
└── README.md

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License

This project is open source under the Apache 2.0 License.

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Author

Afshin Sadeghi
📧 [e-mail]
🔗 GitHub
🔗 Google Scholar
🔗 LinkedIn