LiteLLM

The lab runs a LiteLLM proxy that gives you access to large language models running on the lab's GPU server (orca), using an OpenAI-compatible API. This lets you use tools like Python scripts, curl, and Claude Code with local open-source models without needing an external API account.

Getting access

Email adarsh@arizona.edu to request an API key. Include a brief description of how you plan to use it.

The API base URL is: https://litellm.lab.pyarelal.xyz

Once you have a key, set it as an environment variable so it persists across sessions. Add this to your shell config file (e.g. ~/.bashrc, ~/.zshrc):

export LITELLM_API_KEY=sk-...

Then reload your shell: source ~/.bashrc (or open a new terminal).

Available models

To see which models are currently available:

curl https://litellm.lab.pyarelal.xyz/models \
  -H "Authorization: Bearer $LITELLM_API_KEY"

Models are named <family>:<size>[-a<N>b]-<quant>, e.g. qwen3.6:35b-a3b-q8_0. The name tells you three things: total parameter count (35b), whether it's a mixture-of-experts model (a3b = only 3B parameters active per token; no a-suffix means dense), and the quantization level (q8_0 = 8-bit, near-lossless; q4_k_m = 4-bit). Dense models are generally stronger per total parameter; MoE models generate faster for their size.

Using with curl

curl -X POST https://litellm.lab.pyarelal.xyz/chat/completions \
  -H "Authorization: Bearer $LITELLM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3.6:27b-q8_0",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Using with Python

Install the OpenAI SDK if you don't have it: pip install openai

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["LITELLM_API_KEY"],
    base_url="https://litellm.lab.pyarelal.xyz",
)

response = client.chat.completions.create(
    model="qwen3.6:27b-q8_0",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)

Using with Claude Code

You can use Claude Code with the lab's models by pointing it at LiteLLM instead of Anthropic's API. Set these environment variables before running claude:

export ANTHROPIC_API_KEY=$LITELLM_API_KEY
export ANTHROPIC_BASE_URL=https://litellm.lab.pyarelal.xyz
claude

Then switch to a lab model inside Claude Code with the /model command:

/model qwen3.6:27b-q8_0

Note: open-source models have different capabilities than Claude — some Claude Code features (e.g. complex tool use) may not work as well.