Qwen3.5-35B-A3B-Instruct-int4-mixed-AutoRound

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Language 中文|English

Model Details

This model is an mixed-bits INT4 quantized model with group_size 128 and symmetric quantization of Qwen3.5-35B-A3B generated by intel/auto-round. Please follow the license of the original model. This model uses Instruct mode by default. To enable thinking mode, please change {%- set enable_thinking = false %} to true in chat_template.jinja, and make sure to add --reasoning-parser qwen3 to the model launch command.Like thinking-mode.

Quantization Strategy (Intel MoE Recipe)

Model Comparison

Quickstart

vLLM Usage

vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.

Directly talk to the model

With vLLM already installed, create a file named example.py, copy the example code below into it, and then run python example.py in terminal.

import argparse
import atexit
import json
import os
import shutil
import subprocess
import sys
import time
import urllib.error
import urllib.request


# ---------------------------
# User-facing configuration
# ---------------------------
DEFAULTS = {
    "model": "YCWTG/Qwen3.5-35B-A3B-Instruct-int4-mixed-AutoRound",
    "served_model_name": "YCWTG/Qwen3.5-35B-A3B-Instruct-int4-mixed-AutoRound",
    "host": "localhost",
    "port": 8000,
    "max_model_len": 232320,
    "enable_auto_tool_choice": True,
    "tool_call_parser": "qwen3_coder",
    "attention_backend": "FLASHINFER",
    "max_num_seqs": 1,
    "allowed_local_media_path": None,
    "trust_remote_code": True,
    "language_model_only": False,
}

RUNTIME = {
    "gpu_memory_utilization": 0.98,
    "startup_timeout_sec": 180,
    "healthcheck_timeout_sec": 3,
    "healthcheck_interval_sec": 1,
    "chat_timeout_sec": 600,
}

SERVE_VALUE_OPTIONS = (
    ("--served-model-name", "served_model_name"),
    ("--host", "host"),
    ("--port", "port"),
    ("--max-model-len", "max_model_len"),
    ("--tool-call-parser", "tool_call_parser"),
    ("--attention-backend", "attention_backend"),
    ("--max-num-seqs", "max_num_seqs"),
)

CLIENT_VALUE_OPTIONS = (
    ("--model", "model"),
    *SERVE_VALUE_OPTIONS,
)

SERVE_BOOL_OPTIONS = (
    ("--enable-auto-tool-choice", "enable_auto_tool_choice"),
    ("--trust-remote-code", "trust_remote_code"),
    ("--language-model-only", "language_model_only"),
)

CLIENT_BOOL_OPTIONS = (
    ("--language-model-only", "--no-language-model-only", "language_model_only"),
    ("--enable-auto-tool-choice", "--no-enable-auto-tool-choice", "enable_auto_tool_choice"),
    ("--trust-remote-code", "--no-trust-remote-code", "trust_remote_code"),
)


def append_value_options(cmd, args, options):
    for flag, attr in options:
        cmd.extend([flag, str(getattr(args, attr))])


def append_true_bool_options(cmd, args, options):
    for flag, attr in options:
        if getattr(args, attr):
            cmd.append(flag)


def append_boolean_optional_options(cmd, args, options):
    for positive_flag, negative_flag, attr in options:
        cmd.append(positive_flag if getattr(args, attr) else negative_flag)


def append_optional_value_option(cmd, args, flag, attr):
    value = getattr(args, attr)
    if value is None:
        return
    if isinstance(value, str) and not value.strip():
        return
    cmd.extend([flag, str(value)])


def multiline_input():
    print('User (type "END" on a single line to send, "exit" to quit):')
    lines = []
    while True:
        line = input()
        text = line.strip()
        if text.lower() in {"exit", "quit"}:
            return None
        if text == "END":
            break
        lines.append(line)
    return "\n".join(lines)


def resolve_client_host(host):
    return "127.0.0.1" if host in {"0.0.0.0", "::"} else host


def launch_vllm(args):
    cmd = ["vllm", "serve", args.model]
    append_value_options(cmd, args, SERVE_VALUE_OPTIONS)
    append_optional_value_option(cmd, args, "--allowed-local-media-path", "allowed_local_media_path")
    cmd.extend(
        [
            "--gpu-memory-utilization",
            str(RUNTIME["gpu_memory_utilization"]),
        ]
    )
    append_true_bool_options(cmd, args, SERVE_BOOL_OPTIONS)

    print("Launching vLLM:")
    print(" ".join(cmd))
    try:
        return subprocess.Popen(cmd)
    except FileNotFoundError as e:
        raise RuntimeError("vllm command not found. Activate an environment that has vllm installed.") from e


def stop_vllm(proc):
    if proc and proc.poll() is None:
        proc.terminate()
        try:
            proc.wait(timeout=10)
        except subprocess.TimeoutExpired:
            proc.kill()


def wait_vllm_ready(base_url, timeout_sec=RUNTIME["startup_timeout_sec"]):
    deadline = time.time() + timeout_sec
    url = f"{base_url}/v1/models"
    req = urllib.request.Request(url=url)
    while time.time() < deadline:
        try:
            with urllib.request.urlopen(req, timeout=RUNTIME["healthcheck_timeout_sec"]) as resp:
                if resp.status == 200:
                    return True
        except urllib.error.URLError:
            pass
        time.sleep(RUNTIME["healthcheck_interval_sec"])
    return False


def chat_once(base_url, model_name, messages):
    payload = {"model": model_name, "messages": messages}
    req = urllib.request.Request(
        url=f"{base_url}/v1/chat/completions",
        data=json.dumps(payload, ensure_ascii=False).encode("utf-8"),
        headers={"Content-Type": "application/json"},
        method="POST",
    )
    with urllib.request.urlopen(req, timeout=RUNTIME["chat_timeout_sec"]) as resp:
        data = json.loads(resp.read().decode("utf-8"))
    return data["choices"][0]["message"]


def chat_loop(base_url, model_name):
    print("\n===== Chat Started =====\n")
    messages = []

    while True:
        user_text = multiline_input()
        if user_text is None:
            break

        messages.append({"role": "user", "content": user_text})
        try:
            assistant_msg = chat_once(base_url, model_name, messages)
        except Exception as e:
            print(f"\nRequest failed: {e}\n")
            messages.pop()
            continue

        content = assistant_msg.get("content")
        tool_calls = assistant_msg.get("tool_calls")

        if content:
            print(f"\nAssistant:\n{content}\n")
        elif tool_calls:
            print("\nAssistant(tool_calls):")
            print(json.dumps(tool_calls, ensure_ascii=False, indent=2))
            print()
        else:
            print("\nAssistant:\n(empty response)\n")

        normalized_msg = {"role": "assistant", "content": content or ""}
        if tool_calls:
            normalized_msg["tool_calls"] = tool_calls
        messages.append(normalized_msg)


def build_client_command(args):
    cmd = [sys.executable, os.path.abspath(__file__), "--_client"]
    append_value_options(cmd, args, CLIENT_VALUE_OPTIONS)
    append_boolean_optional_options(cmd, args, CLIENT_BOOL_OPTIONS)
    return cmd


def spawn_chat_terminal(args):
    client_cmd = build_client_command(args)

    terminal_cmd = None
    if os.name == "nt":
        # Open a new cmd window on Windows and keep it alive for interactive chat.
        terminal_cmd = [
            "cmd",
            "/c",
            "start",
            "",
            "cmd",
            "/k",
            subprocess.list2cmdline(client_cmd),
        ]
    elif shutil.which("gnome-terminal"):
        terminal_cmd = ["gnome-terminal", "--", *client_cmd]
    elif shutil.which("x-terminal-emulator"):
        terminal_cmd = ["x-terminal-emulator", "-e", *client_cmd]

    if not terminal_cmd:
        return False

    try:
        subprocess.Popen(terminal_cmd)
        return True
    except Exception as e:
        print(f"Failed to open a new terminal automatically: {e}")
        return False


def parse_args():
    parser = argparse.ArgumentParser(description="Minimal local vLLM chat script")
    parser.add_argument("--_client", action="store_true", help=argparse.SUPPRESS)
    parser.add_argument("--model", default=DEFAULTS["model"])
    parser.add_argument(
        "--served-model-name",
        default=DEFAULTS["served_model_name"],
    )
    parser.add_argument("--host", default=DEFAULTS["host"])
    parser.add_argument("--port", type=int, default=DEFAULTS["port"])
    parser.add_argument("--max-model-len", type=int, default=DEFAULTS["max_model_len"])
    parser.add_argument(
        "--max-num-seqs",
        "--max_num_seqs",
        dest="max_num_seqs",
        type=int,
        default=DEFAULTS["max_num_seqs"],
    )
    parser.add_argument(
        "--enable-auto-tool-choice",
        action=argparse.BooleanOptionalAction,
        default=DEFAULTS["enable_auto_tool_choice"],
    )
    parser.add_argument(
        "--allowed-local-media-path",
        default=DEFAULTS["allowed_local_media_path"],
        help="Optional local media path. Leave empty to disable.",
    )
    parser.add_argument("--tool-call-parser", default=DEFAULTS["tool_call_parser"])
    parser.add_argument("--attention-backend", default=DEFAULTS["attention_backend"])
    parser.add_argument(
        "--trust-remote-code",
        action=argparse.BooleanOptionalAction,
        default=DEFAULTS["trust_remote_code"],
    )
    parser.add_argument(
        "--language-model-only",
        action=argparse.BooleanOptionalAction,
        default=DEFAULTS["language_model_only"],
    )
    return parser.parse_args()


def main():
    args = parse_args()
    base_url = f"http://{resolve_client_host(args.host)}:{args.port}"
    if args._client:
        chat_loop(base_url, args.served_model_name)
        return

    proc = launch_vllm(args)
    atexit.register(stop_vllm, proc)

    print(f"Waiting for service to become ready: {base_url}")
    if not wait_vllm_ready(base_url):
        print("vLLM startup timed out. Check server logs above.")
        stop_vllm(proc)
        sys.exit(1)

    if spawn_chat_terminal(args):
        print("Model is ready. Opened a new terminal for chat; this terminal keeps server logs.")
        print("Press Ctrl+C here to stop vLLM.")
        try:
            proc.wait()
        except KeyboardInterrupt:
            print("\nInterrupted. Stopping vLLM...")
    else:
        print("No supported terminal found. Falling back to chat in this terminal.")
        chat_loop(base_url, args.served_model_name)


if __name__ == "__main__":
    main()

Directly use the OpenAPI

Instruct Mode

vllm serve --model YCWTG/Qwen3.5-35B-A3B-Instruct-int4-mixed-AutoRound --served-model-name YCWTG/Qwen3.5-35B-A3B-Instruct-int4-mixed-AutoRound --host localhost --port 8000 --max-model-len 232320 --enable-auto-tool-choice --tool-call-parser qwen3_coder --trust-remote-code --gpu-memory-utilization 0.98 --attention-backend FLASHINFER --max_num_seqs 1 --allowed-local-media-path /home/ycwtg/image

Thinking Mode

vllm serve --model YCWTG/Qwen3.5-35B-A3B-Instruct-int4-mixed-AutoRound --served-model-name YCWTG/Qwen3.5-35B-A3B-Instruct-int4-mixed-AutoRound --host localhost --port 8000 --max-model-len 232320 --enable-auto-tool-choice --tool-call-parser qwen3_coder --trust-remote-code --gpu-memory-utilization 0.98 --attention-backend FLASHINFER --max_num_seqs 1 --allowed-local-media-path /home/ycwtg/image --reasoning-parser qwen3

See its documentation for more details.

The following will create API endpoints at http://localhost:8000/v1.

Generate the Model

See code here.

Ethical Considerations and Limitations

The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.

Therefore, before deploying any applications of the model, developers should perform safety testing.

Caveats and Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.

Here are a couple of useful links to learn more about Intel's AI software:

• Intel Neural Compressor
• AutoRound

Disclaimer

The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.

Cite

@article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} }

arxiv