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YCWTG
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Model Details
Model Provider
YCWTG
Model Tree
Qwen/Qwen3-Coder-30B-A3B-Instruct
Input Modalities
Output Modalities
Supported Functionality
This model is an mixed-bits INT4 quantized model with group_size 128 and symmetric quantization of Qwen/Qwen3-Coder-30B-A3B-Instruct generated by intel/auto-round. Please follow the license of the original model.
| Layer Type | Bits | Notes |
|---|---|---|
| Expert layers (128 experts) | 4-bit | MoE expert MLPs |
| Non-expert layers (attention, gate) | 16-bit | Higher precision for quality |
| shared_expert_gate | 16-bit | Skipped (shape not divisible by 32) |
| lm_head | Original | Excluded by AutoRound |
| Bits | Model Size |
|---|---|
| Original BF16 | ~60GB |
| mixed INT4 | ~20GB (66% reduction ↓↓) |
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.
python
import argparseimport atexitimport jsonimport osimport shutilimport subprocessimport sysimport timeimport urllib.errorimport urllib.request 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, api_key): cmd = [ "vllm", "serve", args.model, "--served-model-name", args.served_model_name, "--host", args.host, "--port", str(args.port), "--max-model-len", str(args.max_model_len), "--tool-call-parser", args.tool_call_parser, "--attention-backend", args.attention_backend, "--api-key", api_key, ] if args.enable_auto_tool_choice: cmd.append("--enable-auto-tool-choice") 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, api_key, timeout_sec=180): deadline = time.time() + timeout_sec url = f"{base_url}/v1/models" req = urllib.request.Request(url=url, headers={"Authorization": f"Bearer {api_key}"}) while time.time() < deadline: try: with urllib.request.urlopen(req, timeout=3) as resp: if resp.status == 200: return True except urllib.error.URLError: pass time.sleep(1) return False def chat_once(base_url, model_name, messages, api_key): 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", "Authorization": f"Bearer {api_key}", }, method="POST", ) with urllib.request.urlopen(req, timeout=600) as resp: data = json.loads(resp.read().decode("utf-8")) return data["choices"][0]["message"] def chat_loop(base_url, model_name, api_key): 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, api_key) 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", "--model", args.model, "--served-model-name", args.served_model_name, "--host", args.host, "--port", str(args.port), "--max-model-len", str(args.max_model_len), "--tool-call-parser", args.tool_call_parser, "--attention-backend", args.attention_backend, "--enable-auto-tool-choice" if args.enable_auto_tool_choice else "--no-enable-auto-tool-choice", ] return cmd def spawn_chat_terminal(args, api_key): client_cmd = build_client_command(args) env = os.environ.copy() env["VLLM_API_KEY"] = api_key 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, env=env) 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="YCWTG/Qwen3-Coder-30B-A3B-Instruct-int4-mixed-AutoRound") parser.add_argument( "--served-model-name", default="YCWTG/Qwen3-Coder-30B-A3B-Instruct-int4-mixed-AutoRound", ) parser.add_argument("--host", default="0.0.0.0") parser.add_argument("--port", type=int, default=8000) parser.add_argument("--max-model-len", type=int, default=106666) parser.add_argument( "--enable-auto-tool-choice", action=argparse.BooleanOptionalAction, default=True, ) parser.add_argument("--tool-call-parser", default="qwen3_coder") parser.add_argument("--attention-backend", default="FLASHINFER") return parser.parse_args() def main(): args = parse_args() api_key = os.environ.get("VLLM_API_KEY") or "local-dev-key" base_url = f"http://{resolve_client_host(args.host)}:{args.port}" if args._client: chat_loop(base_url, args.served_model_name, api_key) return proc = launch_vllm(args, api_key) atexit.register(stop_vllm, proc) print(f"Waiting for service to become ready: {base_url}") if not wait_vllm_ready(base_url, api_key): print("vLLM startup timed out. Check server logs above.") stop_vllm(proc) sys.exit(1) if spawn_chat_terminal(args, api_key): 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, api_key) if __name__ == "__main__": main()Directly use the OpenAPI
shell
vllm serve --model YCWTG/Qwen3-Coder-30B-A3B-Instruct-int4-mixed-AutoRound --host localhost --port 8000 --max-model-len 106666 --enable-auto-tool-choice --tool-call-parser qwen3_coderSee its documentation for more details.
The following will create API endpoints at http://localhost:8000/v1.
python
from auto_round import AutoRound model_name = "Qwen/Qwen3-Coder-30B-A3B-Instruct" # Build layer config for mixed-bits (Intel recipe)layer_config = {}for i in range(48): # 48 layers prefix = f"model.layers.{i}" # Attention layers -> 16-bit for proj in ["q_proj", "k_proj", "v_proj", "o_proj"]: layer_config[f"{prefix}.self_attn.{proj}"] = {"bits": 16} # MLP gate -> 16-bit layer_config[f"{prefix}.mlp.gate"] = {"bits": 16} autoround = AutoRound( model_name, bits=4, # Default for experts dataset="github-code-clean", group_size=128, sym=True, iters=1000, nsamples=512, lr=2e-3, layer_config=layer_config, low_gpu_mem_usage=True)output_dir="~/model/YCWTG--Qwen3-Coder-30B-A3B-Instruct-int4-mixed-AutoRound"autoround.quantize_and_save(output_dir,format="auto_round" )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.
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:
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.
@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} }
import argparseimport atexitimport jsonimport osimport shutilimport subprocessimport sysimport timeimport urllib.errorimport urllib.request 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, api_key): cmd = [ "vllm", "serve", args.model, "--served-model-name", args.served_model_name, "--host", args.host, "--port", str(args.port), "--max-model-len", str(args.max_model_len), "--tool-call-parser", args.tool_call_parser, "--attention-backend", args.attention_backend, "--api-key", api_key, ] if args.enable_auto_tool_choice: cmd.append("--enable-auto-tool-choice") 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, api_key, timeout_sec=180): deadline = time.time() + timeout_sec url = f"{base_url}/v1/models" req = urllib.request.Request(url=url, headers={"Authorization": f"Bearer {api_key}"}) while time.time() < deadline: try: with urllib.request.urlopen(req, timeout=3) as resp: if resp.status == 200: return True except urllib.error.URLError: pass time.sleep(1) return False def chat_once(base_url, model_name, messages, api_key): 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", "Authorization": f"Bearer {api_key}", }, method="POST", ) with urllib.request.urlopen(req, timeout=600) as resp: data = json.loads(resp.read().decode("utf-8")) return data["choices"][0]["message"] def chat_loop(base_url, model_name, api_key): 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, api_key) 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", "--model", args.model, "--served-model-name", args.served_model_name, "--host", args.host, "--port", str(args.port), "--max-model-len", str(args.max_model_len), "--tool-call-parser", args.tool_call_parser, "--attention-backend", args.attention_backend, "--enable-auto-tool-choice" if args.enable_auto_tool_choice else "--no-enable-auto-tool-choice", ] return cmd def spawn_chat_terminal(args, api_key): client_cmd = build_client_command(args) env = os.environ.copy() env["VLLM_API_KEY"] = api_key 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, env=env) 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="YCWTG/Qwen3-Coder-30B-A3B-Instruct-int4-mixed-AutoRound") parser.add_argument( "--served-model-name", default="YCWTG/Qwen3-Coder-30B-A3B-Instruct-int4-mixed-AutoRound", ) parser.add_argument("--host", default="0.0.0.0") parser.add_argument("--port", type=int, default=8000) parser.add_argument("--max-model-len", type=int, default=106666) parser.add_argument( "--enable-auto-tool-choice", action=argparse.BooleanOptionalAction, default=True, ) parser.add_argument("--tool-call-parser", default="qwen3_coder") parser.add_argument("--attention-backend", default="FLASHINFER") return parser.parse_args() def main(): args = parse_args() api_key = os.environ.get("VLLM_API_KEY") or "local-dev-key" base_url = f"http://{resolve_client_host(args.host)}:{args.port}" if args._client: chat_loop(base_url, args.served_model_name, api_key) return proc = launch_vllm(args, api_key) atexit.register(stop_vllm, proc) print(f"Waiting for service to become ready: {base_url}") if not wait_vllm_ready(base_url, api_key): print("vLLM startup timed out. Check server logs above.") stop_vllm(proc) sys.exit(1) if spawn_chat_terminal(args, api_key): 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, api_key) if __name__ == "__main__": main()from auto_round import AutoRound model_name = "Qwen/Qwen3-Coder-30B-A3B-Instruct" # Build layer config for mixed-bits (Intel recipe)layer_config = {}for i in range(48): # 48 layers prefix = f"model.layers.{i}" # Attention layers -> 16-bit for proj in ["q_proj", "k_proj", "v_proj", "o_proj"]: layer_config[f"{prefix}.self_attn.{proj}"] = {"bits": 16} # MLP gate -> 16-bit layer_config[f"{prefix}.mlp.gate"] = {"bits": 16} autoround = AutoRound( model_name, bits=4, # Default for experts dataset="github-code-clean", group_size=128, sym=True, iters=1000, nsamples=512, lr=2e-3, layer_config=layer_config, low_gpu_mem_usage=True)output_dir="~/model/YCWTG--Qwen3-Coder-30B-A3B-Instruct-int4-mixed-AutoRound"autoround.quantize_and_save(output_dir,format="auto_round" )vllm serve --model YCWTG/Qwen3-Coder-30B-A3B-Instruct-int4-mixed-AutoRound --host localhost --port 8000 --max-model-len 106666 --enable-auto-tool-choice --tool-call-parser qwen3_coder