club-3090/docs/EXAMPLES.md

Client examples

Copy-pasteable snippets for talking to the club-3090 endpoint. The default URL is http://localhost:8020; the served model name is qwen3.6-27b-autoround (vLLM) or qwen3.6-27b-autoround (llama.cpp via the --alias flag we set).

All examples assume:

• Server running: bash scripts/launch.sh is up
• API endpoint: http://localhost:8020 (override with OPENAI_BASE_URL env var or client-side base_url)

The endpoint is OpenAI-compatible — anything that speaks OpenAI's /v1/chat/completions API works without modification, just point base_url at the local endpoint.

---

max_tokens defaults — important if you've enabled thinking

Qwen3.6-27B is a thinking model. The <think>...</think> block before the answer routinely runs 2-4K tokens on medium reasoning, 4-8K on harder coding problems, and can exceed 16K on competition-grade problems. If max_tokens cuts the response off mid-think, the model never reaches the answer and the request looks like an "empty response" or truncated garbage. We hit this exact trap on our LiveCodeBench v6 baseline (docs/STRUCTURED_COT.md caveat section).

Use these defaults:

Scenario	max_tokens
FREE thinking on (default long-text / long-vision composes)	8192 minimum. 16384 for hard reasoning / competition-grade problems.
FSM bounded thinking (bounded-thinking.yml)	4096 is comfortable. The recommended DeepSeek scratchpad grammar uses ~500-1000 think tokens; the andthattoo G/A/E grammar uses ~150. Either fits well below 4096.
enable_thinking: False	Set as tight as the answer needs (50-200 typically).
Tool-using agents (multi-turn)	1024-2048 per turn. If a middle turn needs >2K to think, your prompt structure probably needs work.

The smoke-test examples below use max_tokens: 200 because they ask short questions where thinking + answer fits comfortably. Real workloads should follow the table above.

---

Quick curl sanity test

curl -sf http://localhost:8020/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen3.6-27b-autoround",
    "messages": [{"role": "user", "content": "Capital of France?"}],
    "max_tokens": 200
  }' | jq -r '.choices[0].message.content'

Expected response: a sentence containing Paris. The max_tokens: 200 headroom is intentional — Qwen3.6 thinks before answering by default, so even simple questions burn ~50–150 tokens inside <think>...</think> before reaching the answer. Set tighter (max_tokens: 30) only if you also pass chat_template_kwargs: {"enable_thinking": false} to skip the think block — that's what verify-full.sh does internally.

---

Python — openai SDK (recommended)

pip install openai

Basic chat

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8020/v1", api_key="not-needed")

resp = client.chat.completions.create(
    model="qwen3.6-27b-autoround",
    messages=[{"role": "user", "content": "Write a haiku about tensor cores."}],
    max_tokens=120,
    temperature=0.6,
    top_p=0.95,
)
print(resp.choices[0].message.content)

Streaming

stream = client.chat.completions.create(
    model="qwen3.6-27b-autoround",
    messages=[{"role": "user", "content": "Explain attention in 100 words."}],
    max_tokens=300,
    stream=True,
)
for chunk in stream:
    delta = chunk.choices[0].delta.content or ""
    print(delta, end="", flush=True)
print()

Tool calling

Works on both engines (vLLM with --tool-call-parser qwen3_coder and llama.cpp with --jinja — both ship enabled in the default composes):

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather in a city",
            "parameters": {
                "type": "object",
                "properties": {"city": {"type": "string"}},
                "required": ["city"],
            },
        },
    }
]

resp = client.chat.completions.create(
    model="qwen3.6-27b-autoround",
    messages=[{"role": "user", "content": "What's the weather in Tokyo?"}],
    tools=tools,
    tool_choice="auto",
    max_tokens=200,
)

msg = resp.choices[0].message
if msg.tool_calls:
    for tc in msg.tool_calls:
        print(f"Call {tc.function.name}({tc.function.arguments})")
else:
    print(msg.content)

Vision (image input)

vLLM and llama.cpp both auto-load the vision tower / mmproj when configured. Send images as base64 or URLs:

import base64
from pathlib import Path

img_b64 = base64.b64encode(Path("photo.png").read_bytes()).decode()

resp = client.chat.completions.create(
    model="qwen3.6-27b-autoround",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Describe this image."},
                {
                    "type": "image_url",
                    "image_url": {"url": f"data:image/png;base64,{img_b64}"},
                },
            ],
        }
    ],
    max_tokens=200,
    extra_body={"chat_template_kwargs": {"enable_thinking": False}},
)
print(resp.choices[0].message.content)

Reasoning mode (vLLM with Genesis only)

resp = client.chat.completions.create(
    model="qwen3.6-27b-autoround",
    messages=[{"role": "user", "content": "Solve: 7x + 14 = 49. Show your reasoning."}],
    max_tokens=2048,  # FREE thinking on; 2048 fits easy math comfortably. Bump to 8192 for harder reasoning.
    extra_body={"chat_template_kwargs": {"enable_thinking": True}},
)
msg = resp.choices[0].message
print("Reasoning:", getattr(msg, "reasoning_content", "") or "(empty)")
print("Answer:   ", msg.content)

Note: llama.cpp emits the <think>...</think> tokens inline rather than peeling them into a separate reasoning_content field. If you need that split, post-process client-side or stick with vLLM.

---

Python — requests (no SDK)

For environments where you can't install the openai package:

import requests, json

resp = requests.post(
    "http://localhost:8020/v1/chat/completions",
    headers={"Content-Type": "application/json"},
    json={
        "model": "qwen3.6-27b-autoround",
        "messages": [{"role": "user", "content": "What is 17 × 23?"}],
        "max_tokens": 50,
    },
    timeout=60,
)
print(resp.json()["choices"][0]["message"]["content"])

For streaming, use stream=True and parse SSE lines:

with requests.post(
    "http://localhost:8020/v1/chat/completions",
    headers={"Content-Type": "application/json"},
    json={"model": "qwen3.6-27b-autoround", "messages": [...], "stream": True, "max_tokens": 200},
    stream=True,
) as r:
    for line in r.iter_lines():
        if not line or not line.startswith(b"data: "):
            continue
        payload = line[6:]
        if payload == b"[DONE]":
            break
        chunk = json.loads(payload)
        delta = chunk["choices"][0]["delta"].get("content", "")
        print(delta, end="", flush=True)

---

TypeScript / Node — openai SDK

npm install openai

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "http://localhost:8020/v1",
  apiKey: "not-needed",
});

const resp = await client.chat.completions.create({
  model: "qwen3.6-27b-autoround",
  messages: [{ role: "user", content: "Quicksort in Rust, please." }],
  // FREE thinking is on by default. 4096 covers easy code-gen think+answer;
  // 8192 is the safe default for harder coding problems. 800 traps mid-think.
  max_tokens: 4096,
  temperature: 0.6,
  top_p: 0.95,
});

console.log(resp.choices[0].message.content);

Streaming:

const stream = await client.chat.completions.create({
  model: "qwen3.6-27b-autoround",
  messages: [{ role: "user", content: "..." }],
  max_tokens: 300,
  stream: true,
});

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? "");
}
process.stdout.write("\n");

---

Connecting third-party clients

Open WebUI

Settings → Connections → Add OpenAI Connection:

• Base URL: http://localhost:8020/v1 (or http://<host-ip>:8020/v1 from another machine on your LAN — see Security)
• API Key: anything (e.g. sk-local) — the server doesn't check it
• Model: qwen3.6-27b-autoround

Vision, tool calling, streaming all work through the WebUI's standard flows.

Cline / Roo (VS Code agentic coder)

In the Cline settings panel:

• API Provider: OpenAI Compatible
• Base URL: http://localhost:8020/v1
• API Key: sk-local (any non-empty string)
• Model ID: qwen3.6-27b-autoround

Cline sends large tool returns (file reads, web fetches) up to ~25K tokens. As of 2026-05-02 PM (Genesis v7.69 dev tip + vllm#35975 backport), vllm/long-text (180K balanced + MTP K=3) handles these cleanly — 33K AND 50K tool-prefill stress PASS, and 60K single-prompt prefill PASS (the Cliff 2 wall closed at 60K). For one-shot prompts beyond 60K, switch to llamacpp/default (262K, slower) or dual-turbo.yml (262K + 4 streams). See docs/SINGLE_CARD.md, docs/CLIFFS.md, and the VRAM diagram.

Cursor

Settings → Models → Add OpenAI-compatible:

• Override OpenAI Base URL: http://localhost:8020/v1
• Verify config: click "Verify" — should list qwen3.6-27b-autoround
• Model name: qwen3.6-27b-autoround

Cursor's "Apply" feature works against this model since tool-calling is supported.

LiteLLM proxy / aider / Continue.dev

All work the same way — OpenAI-compatible endpoint at http://localhost:8020/v1, any non-empty API key. Confirmed working with the default compose.

---

Security note: network binding

The default composes bind to 0.0.0.0:8020 so other machines on your LAN can connect. If you're on a shared / coffee-shop / coworking network, that exposes your model to anyone who can route to your machine.

To restrict to localhost-only:

# In any docker-compose.*.yml under ports:
ports:
  - "127.0.0.1:8020:8000"   # was: "8020:8000"

Or override at run-time with --host 127.0.0.1 (llama.cpp) / by editing the compose locally.

---

See also

• models/qwen3.6-27b/README.md — variant matrix + VRAM diagram
• docs/SINGLE_CARD.md and docs/DUAL_CARD.md — workload → recommended compose
• scripts/launch.sh — interactive variant picker
• scripts/health.sh — runtime health probe