AISE1_CLASS/AISE501 LLM Zugang/STUDENT_GUIDE.md

Student Guide — Qwen3.5 Inference Server

Overview

A Qwen3.5 large language model is running on our GPU server. Two models may be available at different times (your instructor will let you know which one is active):

Model	Params	Best for
qwen3.5-35b-a3b	35B (3B active)	Fast responses, everyday tasks
qwen3.5-122b-a10b-fp8	122B (10B active)	Complex reasoning, coding, research

There are three ways to interact with the model:

1. Open WebUI — ChatGPT-like interface in your browser (easiest)
2. Streamlit App — Local app with chat, file editor, and code execution
3. Python SDK / curl — Programmatic access via the OpenAI-compatible API

Note

: You must be on the fhgr network or VPN to reach the server.

Connection Details

Parameter	Value
Open WebUI	http://silicon.fhgr.ch:7081
API Base URL	http://silicon.fhgr.ch:7080/v1
Model	(check Open WebUI model selector or ask your instructor)
API Key	(ask your instructor — may be EMPTY)

Tip

: In Open WebUI, the model dropdown at the top automatically shows whichever model is currently running. For the API, use curl http://silicon.fhgr.ch:7080/v1/models to check.

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Option 1: Open WebUI (Recommended)

The easiest way to chat with the model — no installation required.

Getting Started

1. Make sure you are connected to the university network (or VPN).
2. Open your browser and go to http://silicon.fhgr.ch:7081
3. Click "Sign Up" to create a new account:
   • Enter your name (e.g. your first and last name)
   • Enter your email (use your university email)
   • Choose a password
   • Click "Create Account"
4. After signing up you are logged in automatically.
5. Select the model qwen3.5-35b-a3b from the model dropdown at the top.
6. Type a message and press Enter — you're chatting with the LLM.

Returning Later

• Go to http://silicon.fhgr.ch:7081 and click "Sign In".
• Enter the email and password you used during sign-up.
• All your previous chats are still there.

Features

• Chat history — all conversations are saved on the server and persist across sessions
• Markdown rendering with syntax-highlighted code blocks
• Model selector — auto-discovers available models from the server
• Conversation branching — edit previous messages and explore alternative responses
• File upload — attach files to your messages for the model to analyze
• Search — search across all your past conversations

Tips

• Your account and chat history are stored on the server. You can log in from any device on the university network.
• If you forget your password, ask your instructor to reset it via the Admin Panel.
• The model works best when you provide clear, specific instructions.
• For code tasks, mention the programming language explicitly (e.g. "Write a Python function that...").
• Long conversations use more context. Start a New Chat (top-left button) when switching topics to get faster, more focused responses.

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Option 2: Streamlit App (Chat + File Editor)

A local app with chat, file editing, and Python/LaTeX execution. See the Streamlit section below for setup.

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Option 3: Python SDK / curl

For programmatic access and scripting.

Quick Start with Python

1. Install the OpenAI SDK

pip install openai

2. Simple Chat

from openai import OpenAI

client = OpenAI(
    base_url="http://silicon.fhgr.ch:7080/v1",
    api_key="EMPTY",  # replace if your instructor set a key
)

response = client.chat.completions.create(
    model="qwen3.5-35b-a3b",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain gradient descent in simple terms."},
    ],
    max_tokens=1024,
    temperature=0.7,
)

print(response.choices[0].message.content)

3. Streaming Responses

stream = client.chat.completions.create(
    model="qwen3.5-35b-a3b",
    messages=[
        {"role": "user", "content": "Write a haiku about machine learning."},
    ],
    max_tokens=256,
    stream=True,
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)
print()

---

Quick Start with curl

curl http://silicon.fhgr.ch:7080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3.5-35b-a3b",
    "messages": [
      {"role": "user", "content": "What is the capital of Switzerland?"}
    ],
    "max_tokens": 256,
    "temperature": 0.7
  }'

---

Recommended Parameters

Parameter	Recommended	Notes
temperature	0.7	Lower = more deterministic, higher = creative
max_tokens	1024–4096	Increase for long-form output
top_p	0.95	Nucleus sampling
stream	true	Better UX for interactive use

---

Tips & Etiquette

• Be mindful of context length: Avoid excessively long prompts (>8K tokens) unless necessary.
• Use streaming: Makes responses feel faster and reduces perceived latency.
• Don't spam requests: The server is shared among ~15 students.
• Check the model name: Always use qwen3.5-35b-a3b as the model parameter.

---

Streamlit Chat & File Editor App

A web UI is included for chatting with the model and editing files. It runs on your own machine and connects to the GPU server.

Setup

# Clone the repository
git clone https://gitea.fhgr.ch/herzogfloria/LLM_Inferenz_Server_1.git
cd LLM_Inferenz_Server_1

# Create a virtual environment and install dependencies
python3 -m venv .venv
source .venv/bin/activate        # macOS / Linux
# .venv\Scripts\activate         # Windows
pip install -r requirements.txt

Run

streamlit run app.py

Opens at http://localhost:8501 in your browser.

Features

Chat Tab

• Conversational interface with streaming responses
• "Save code" button extracts code from the LLM response and saves it to a workspace file (strips markdown formatting automatically)

File Editor Tab

• Create and edit .py, .tex, .html, or any text file
• Syntax-highlighted preview of file content
• "Generate with LLM" button: describe a change in natural language and the model rewrites the file (e.g. "add error handling", "fix the LaTeX formatting", "translate comments to German")

Sidebar Controls

• Connection: API Base URL and API Key
• LLM Parameters: Adjustable for each request

Parameter	Default	What it does
Thinking Mode	Off	Toggle chain-of-thought reasoning (better for complex tasks, slower)
Temperature	0.7	Lower = predictable, higher = creative
Max Tokens	4096	Maximum response length
Top P	0.95	Nucleus sampling threshold
Presence Penalty	0.0	Encourage diverse topics

• File Manager: Create new files and switch between them

All generated files are stored in a workspace/ folder next to app.py.

Tip

: The app runs entirely on your local machine. Only the LLM requests go to the server — your files stay local.

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Thinking Mode

By default, the model "thinks" before answering (internal chain-of-thought). This is great for complex reasoning but adds latency for simple questions.

To disable thinking and get faster direct responses, add this to your API call:

response = client.chat.completions.create(
    model="qwen3.5-35b-a3b",
    messages=[...],
    max_tokens=1024,
    extra_body={"chat_template_kwargs": {"enable_thinking": False}},
)

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Troubleshooting

Issue	Solution
Connection refused	Check you're on the university network / VPN
Model not found	Use model name qwen3.5-35b-a3b exactly
Slow responses	The model is shared — peak times may be slower
401 Unauthorized	Ask your instructor for the API key
Response cut off	Increase max_tokens in your request
Open WebUI login fails	Make sure you created an account first (Sign Up)
Open WebUI shows no models	The vLLM server may still be loading — wait a few minutes