qflash/README.md

# Qflash: Free Qwen3.8-Flash-Next LLM gateway

## About

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`:

```bash
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:

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

Alternatively, build directly from source using `make`:

```bash
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:

```bash
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:

```bash
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:

```bash
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):

```bash
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:

```python
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.