Qflash is a standalone, single-binary gateway that exposes the Qwen3.8-Flash-Next Gradio space (https://halvo78-qwen3-8-flash-next-playground.hf.space) through an OpenAI-compatible API. It translates the standard /v1/chat/completions and /v1/models endpoints into Gradio's /gradio_api/call/chat_response request and Server-Sent Events (SSE) stream protocol, allowing any standard OpenAI-compatible client, agent, or IDE to interface with Qwen3.8-Flash-Next without modification.

Features

OpenAI-compatible chat completions (streaming and non-streaming)
Deep reasoning extraction with thinking trace passthrough (<think> tags and blockquotes mapped to reasoning_content)
Stateful streaming tool call interception (StreamToolCallFilter) with zero XML/JSON leakage into delta.content
Real-time token streaming with incremental SSE delivery
Support for instruct mode via standard reasoning_effort: "none"
Zero-dependency SOCKS5 proxy client (RFC 1928 / RFC 1929) with domain resolution (socks5h://), IPv4, IPv6, and auth
Bring Your Own Key (BYOK) pass-through support via Authorization: Bearer or CLI flags
Fibonacci backoff retry on transient upstream errors
Zero external dependencies (Go standard library only)

Installation

You need Go 1.22 or newer (tested on Go 1.26). Install the latest release straight from the Git repository with go install:

go install code.luxferre.top/luxferre/qflash@latest

This fetches the module from https://code.luxferre.top/luxferre/qflash.git and places the qflash binary in $(go env GOPATH)/bin. Make sure that directory is on your PATH.

(Note: If installing right after a new commit has been pushed, bypass any proxy cache with GOPROXY=direct go install code.luxferre.top/luxferre/qflash@latest).

If you prefer to build from a local checkout instead:

git clone https://code.luxferre.top/luxferre/qflash.git
cd qflash
go install .

Alternatively, build directly from source using make:

make qflash

This produces the bin/qflash binary for your platform.

Models served

The gateway advertises the following models under /v1/models:

Model ID	Target model	Description
`Qwen/Qwen3.8-Flash-Next`	`Qwen/Qwen3.8-Flash-Next`	Primary playground model (125B MoE, 6B activated)
`qwen3.8-flash-next`	`Qwen/Qwen3.8-Flash-Next`	Standard lowercase alias
`qwen-flash-next`	`Qwen/Qwen3.8-Flash-Next`	Shorthand alias
`qwen-flash`	`Qwen/Qwen3.8-Flash-Next`	Quick convenience alias

Any unlisted custom model name requested by the client is passed through directly.

Usage

Run the gateway:

qflash [-port 8080] [-endpoint https://halvo78-qwen3-8-flash-next-playground.hf.space] [-model Qwen/Qwen3.8-Flash-Next]

Available flags:

-port — TCP port to listen on (default 8080)
-endpoint — root URL of the Gradio space (default https://halvo78-qwen3-8-flash-next-playground.hf.space)
-model — exposed model name (default Qwen/Qwen3.8-Flash-Next)
-thinking / -enable-thinking — enable chain-of-thought reasoning by default (default true)
-hf-token — optional Hugging Face API token for authenticated spaces (HF_TOKEN env)
-api-key — upstream inference engine API key for BYOK mode (OPENAI_API_KEY / QWEN_API_KEY env)
-base-url — upstream inference engine base URL for BYOK mode (OPENAI_BASE_URL / QWEN_BASE_URL env)
-socks / -proxy / -socks5 — SOCKS5 proxy URL, e.g. socks5://127.0.0.1:1080 (ALL_PROXY env)
-user-agent / -ua — custom User-Agent sent to upstream

Endpoints served:

GET /models and GET /v1/models
POST /chat/completions and POST /v1/chat/completions

Example request with curl:

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen3.8-flash-next","messages":[{"role":"user","content":"Explain QSA micro-blocks in one sentence."}],"stream":false}'

Streaming example:

curl -N http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen-flash","messages":[{"role":"user","content":"Count from 1 to 5."}],"stream":true}'

Instruct mode example (disables thinking):

curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen-flash","messages":[{"role":"user","content":"Hello!"}],"reasoning_effort":"none"}'

Python OpenAI SDK integration:

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8080/v1",
    api_key="sk-dummy"
)

stream = client.chat.completions.create(
    model="qwen3.8-flash-next",
    messages=[
        {"role": "user", "content": "Prove that the sum of the first n odd numbers is n^2."}
    ],
    stream=True
)

for chunk in stream:
    delta = chunk.choices[0].delta
    if hasattr(delta, "reasoning_content") and delta.reasoning_content:
        print(f"[THINK] {delta.reasoning_content}", end="", flush=True)
    if delta.content:
        print(delta.content, end="", flush=True)
print()

Credits

Created by Luxferre in 2026, released into the public domain with no warranties.