LLM_Inferenz_Server_1/STUDENT_GUIDE.md

Student Guide — Qwen3.5-35B-A3B Inference Server

Overview

A Qwen3.5-35B-A3B language model is running on our GPU server. It's a Mixture-of-Experts model (35B total parameters, 3B active per token), providing fast and high-quality responses. You can interact with it using the OpenAI-compatible API.

Connection Details

Parameter	Value
Base URL	http://silicon.fhgr.ch:7080/v1
Model	qwen3.5-35b-a3b
API Key	(ask your instructor — may be EMPTY)

Note

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

---

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.

---

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}},
)

---

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