Qflash is a standalone, single-binary gateway that exposes Qwen3.8 model spaces on Hugging Face through a standard OpenAI-compatible API. It automatically detects and supports multiple upstream protocols:

gradio /respond endpoints (such as https://microhero-qwen3-8-27b-uncensored-chat.hf.space, running live on ZeroGPU)
direct OpenAI /v1/chat/completions endpoints (such as https://wanyamaelis-qwen3-8-27b.hf.space and https://apathy-exe-qwen3-8-flash-next.hf.space, running llama-server on CPU)
gradio /chat_response endpoints (such as https://halvo78-qwen3-8-flash-next-playground.hf.space)

It translates standard /v1/chat/completions and /v1/models requests and Server-Sent Events (SSE) stream protocols into upstream formats, allowing any standard OpenAI-compatible client, agent, or IDE to interface with Qwen3.8 models without modification.

Features

openai-compatible chat completions (streaming and non-streaming)
automatic upstream failover across ZeroGPU and non-ZeroGPU endpoints
smart endpoint cooldown (5 minutes on quota exhaustion, 30 seconds on network errors)
automatic upstream endpoint detection (respond, openai, chat_response)
deep reasoning extraction with thinking trace passthrough (<think> tags and blockquotes mapped to reasoning_content)
stateful streaming tool call interception (StreamToolCallFilter) with zero XML or 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
zero-gpu quota authentication via free Hugging Face personal tokens (HF_TOKEN)
fibonacci backoff retry on transient upstream errors
zero external dependencies (Go standard library only)

Supported upstream spaces

Space	Model	Hardware	Protocol	Notes
`MicroHERO/qwen3.8-27b-uncensored-chat` (primary)	Qwen3.8-27B Uncensored	ZeroGPU (A10G)	Gradio `/respond`	Fast live GPU inference (~2s), uncensored weights
`Wanyamaelis/Qwen3.8-27B` (fallback)	Qwen3.8-27B (MTP)	CPU basic (8 vCPU)	Native OpenAI `/v1`	Non-ZeroGPU, no quota limits, fast speculative decoding (~5s)
`apathy-exe/Qwen3.8-27B`	Qwen3.8-27B (MTP)	CPU (OpenMP/AVX512)	Native OpenAI `/v1`	Non-ZeroGPU, no quota limits, speculative decoding
`apathy-exe/Qwen3.8-Flash-Next`	Qwen3.8-Flash-Next (~177B)	CPU (OpenMP/AVX512)	Native OpenAI `/v1`	Non-ZeroGPU, no quota limits, full 177B Flash-Next model
`Halvo78/qwen3-8-flash-next-playground`	Qwen3.8-Flash-Next	CPU basic	Gradio `/chat_response`	Sandbox client; requires BYOK API key/base URL

Auto-failover and ZeroGPU quota handling

Spaces running on ZeroGPU provide free A10G compute, but anonymous requests share a small pool per IP address. When ZeroGPU runs limit is reached, upstream returns a quota error (429 or ZeroGPU runs limit).

Qflash handles this seamlessly:

with auto-failover enabled (default), when a ZeroGPU space hits its runs limit, it is placed on a 5-minute cooldown and the gateway automatically fails over to the next configured endpoint (e.g. wanyamaelis-qwen3-8-27b, which runs on CPU with zero quota limits)
transient network errors trigger a shorter 30-second cooldown before retrying
you can also provide a free Hugging Face personal access token (https://huggingface.co/settings/tokens) via the HF_TOKEN environment variable, the -hf-token CLI flag, or the Authorization: Bearer hf_... header to authenticate ZeroGPU requests directly

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/Qwen3.8-27B-Uncensored`	`Qwen/Qwen3.8-27B-Uncensored`	Default primary live model
`Qwen/Qwen3.8-Flash-Next`	`Qwen/Qwen3.8-Flash-Next`	Flash-Next model identifier
`qwen3.8-27b-uncensored`	`Qwen/Qwen3.8-27B-Uncensored`	Standard lowercase alias
`qwen3.8-flash-next`	`Qwen/Qwen3.8-Flash-Next`	Flash-Next lowercase alias
`qwen-flash-next`	`Qwen/Qwen3.8-Flash-Next`	Shorthand alias
`qwen-flash`	`Qwen/Qwen3.8-Flash-Next`	Quick convenience alias
`qwen`	Default model	Generic shorthand alias

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

Usage

Run the gateway with default auto-failover endpoints:

qflash

By default, this listens on http://127.0.0.1:8080 with failover configured across MicroHERO (ZeroGPU), wanyamaelis (CPU non-ZeroGPU), and apathy-exe endpoints.

Available flags:

-port — tcp port to listen on (default 8080)
-endpoints / -endpoint — comma-separated upstream space or OpenAI URLs (default list of 4 endpoints, QFLASH_ENDPOINTS / QFLASH_ENDPOINT env)
-failover / -auto-failover — enable automatic failover across endpoints on quota exhaustion or error (default true, QFLASH_FAILOVER env)
-mode — upstream protocol mode: auto, respond, chat_response, openai (default auto, QFLASH_MODE env)
-model — exposed model name (default Qwen/Qwen3.8-27B-Uncensored, QFLASH_MODEL env)
-thinking / -enable-thinking — enable chain-of-thought reasoning by default (default true)
-hf-token / -token — Hugging Face API token for ZeroGPU quota or private 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":"qwen","messages":[{"role":"user","content":"Explain quantum superposition in one sentence."}],"stream":false}'

Streaming example:

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

Using custom upstream spaces or a single endpoint:

# Point to a single CPU llama-server without failover
qflash -endpoint https://wanyamaelis-qwen3-8-27b.hf.space -failover=false

# Point to Halvo78 playground with your own API credentials
qflash -endpoint https://halvo78-qwen3-8-flash-next-playground.hf.space -api-key "sk-..." -base-url "https://api.openai.com/v1"

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="qwen",
    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.