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YCWTG
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Model Details
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YCWTG
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Qwen/Qwen3-Coder-Next
Input Modalities
Output Modalities
Supported Functionality
This model is an mixed-bits INT2 quantized model with group_size 512 and symmetric quantization of Qwen/Qwen3-Coder-Next generated by intel/auto-round. Please follow the license of the original model.
| Layer Type | Bits | Notes |
|---|---|---|
| Expert layers (512 experts) | 2-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 |
With Transformers 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 mathimport os os.environ.setdefault( "PYTORCH_ALLOC_CONF", # Keep a safer allocator default and transparently migrate the deprecated env var. os.environ.pop("PYTORCH_CUDA_ALLOC_CONF", None) or "expandable_segments:True",) import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer MODEL_NAME = "YCWTG/Qwen3-Coder-Next-int2-mixed-AutoRound"AUTO_MAX_TOKENS = TrueMANUAL_MAX_NEW_TOKENS = 128AUTO_MAX_TOKENS_RATIO = 1.5 HAS_CUDA = torch.cuda.is_available()# Read total VRAM once and use it to choose the default loading mode.GPU_TOTAL_MIB = torch.cuda.get_device_properties(0).total_memory // (1024 ** 2) if HAS_CUDA else 0# 32GB-class GPUs default to False; smaller GPUs default to True.ENABLE_CPU_OFFLOAD = HAS_CUDA and GPU_TOTAL_MIB < 32000MAX_MEMORY = {0: "18GiB", "cpu": "64GiB"} if ENABLE_CPU_OFFLOAD else {0: "22GiB", "cpu": "16GiB"} def get_input_device(model): # With device_map="auto", the first usable device may not be model.device. device_map = getattr(model, "hf_device_map", None) cpu_device = None if isinstance(device_map, dict): for loc in device_map.values(): if isinstance(loc, int): return torch.device(f"cuda:{loc}") if isinstance(loc, str): if loc.startswith("cuda"): return torch.device(loc) if loc.startswith("cpu"): cpu_device = torch.device("cpu") return cpu_device or next(model.parameters()).device def load_model(): print("Loading model...") tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True, use_fast=True) # Use EOS as PAD to avoid warnings for chat generation on models without a pad token. tokenizer.pad_token = tokenizer.pad_token or tokenizer.eos_token tokenizer.padding_side = "left" model_kwargs = { "pretrained_model_name_or_path": MODEL_NAME, "dtype": torch.bfloat16, "trust_remote_code": True, "low_cpu_mem_usage": True, "device_map": "auto" if HAS_CUDA else "cpu", } if HAS_CUDA: print(f"GPU total memory: {GPU_TOTAL_MIB} MiB") model_kwargs["max_memory"] = MAX_MEMORY if ENABLE_CPU_OFFLOAD: model_kwargs["offload_buffers"] = True print("CPU offload: ON") else: print("CPU offload: OFF (GPU preferred, small CPU spill enabled)") else: print("CUDA not available, running on CPU") try: model = AutoModelForCausalLM.from_pretrained(**model_kwargs) except RuntimeError as e: # Give a beginner-friendly hint instead of only showing the raw stack trace. if "out of memory" in str(e).lower(): print("\nCUDA OOM while loading the model.") print("Close other GPU programs, or set ENABLE_CPU_OFFLOAD = True and run again.") raise model.eval() return model, tokenizer def multiline_input(): print('User (type "END" on a single line to send, type "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 build_input_ids(tokenizer, messages, device): if getattr(tokenizer, "chat_template", None): # Preferred path for chat models: let the tokenizer build the prompt format. prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) else: # Generic fallback for tokenizers without a built-in chat template. prompt = "\n".join( [f"{'User' if m['role'] == 'user' else 'Assistant'}: {m['content']}" for m in messages] + ["Assistant:"] ) return tokenizer(prompt, return_tensors="pt")["input_ids"].to(device) def chat_loop(model, tokenizer): print("\n===== Chat Started =====\n") print(f"Auto max_tokens: {'ON' if AUTO_MAX_TOKENS else 'OFF'}") if not AUTO_MAX_TOKENS: print(f"Manual max_new_tokens: {MANUAL_MAX_NEW_TOKENS}") print( "Tip: Set ENABLE_CPU_OFFLOAD = False for a faster full-GPU attempt." if ENABLE_CPU_OFFLOAD else "Tip: If max_tokens is too large and you hit CUDA OOM, set ENABLE_CPU_OFFLOAD = True." ) messages = [] device = get_input_device(model) print(f"Input device: {device}") while True: user_text = multiline_input() if user_text is None: break messages.append({"role": "user", "content": user_text}) input_ids = build_input_ids(tokenizer, messages, device) prompt_tokens = int(input_ids.shape[-1]) # Auto mode scales output length with prompt length (1.5x by default). max_new_tokens = max(1, math.ceil(prompt_tokens * AUTO_MAX_TOKENS_RATIO)) if AUTO_MAX_TOKENS else int(MANUAL_MAX_NEW_TOKENS) print(f"Prompt tokens: {prompt_tokens}") print(f"max_new_tokens: {max_new_tokens}") try: with torch.inference_mode(): output_ids = model.generate( input_ids=input_ids, max_new_tokens=max_new_tokens, do_sample=True, temperature=1.0, top_p=0.95, top_k=40, use_cache=False, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, ) except RuntimeError as e: error_text = str(e).lower() if HAS_CUDA and ("cublas_status_alloc_failed" in error_text or "out of memory" in error_text): # Clear cached blocks so the next try starts from a cleaner CUDA state. torch.cuda.empty_cache() print("\nCUDA OOM during generation.") print("Set ENABLE_CPU_OFFLOAD = True, or disable AUTO_MAX_TOKENS and lower MANUAL_MAX_NEW_TOKENS.") messages.pop() continue raise reply_text = tokenizer.decode(output_ids[0, input_ids.shape[-1]:], skip_special_tokens=True) print(f"\nAssistant:\n{reply_text}\n") messages.append({"role": "assistant", "content": reply_text}) if __name__ == "__main__": model, tokenizer = load_model() chat_loop(model, tokenizer)python
from auto_round import AutoRound model_name = "Qwen/Qwen3-Coder-Next" # 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 if i in [3, 7, 11, 15, 19, 23, 27, 31, 35, 39, 43, 47]: # self_attn layers for proj in ["q_proj", "k_proj", "v_proj", "o_proj"]: layer_config[f"{prefix}.self_attn.{proj}"] = {"bits": 16} else: # linear_attn layers -> 16-bit for proj in ["in_proj_qkvz", "in_proj_ba", "out_proj"]: layer_config[f"{prefix}.linear_attn.{proj}"] = {"bits": 16} # MLP gate -> 16-bit layer_config[f"{prefix}.mlp.gate"] = {"bits": 16} # shared_expert_gate -> 16-bit (skipped) layer_config[f"{prefix}.mlp.shared_expert_gate"] = {"bits": 16} autoround = AutoRound( model_name, bits=2, # Default for experts group_size=128, sym=True, iters=1000, nsamples=512, lr=2e-3, layer_config=layer_config, low_gpu_mem_usage=True, enable_alg_ext=True)output_dir="~/model/YCWTG--Qwen3-Coder-Next-int2-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 mathimport os os.environ.setdefault( "PYTORCH_ALLOC_CONF", # Keep a safer allocator default and transparently migrate the deprecated env var. os.environ.pop("PYTORCH_CUDA_ALLOC_CONF", None) or "expandable_segments:True",) import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer MODEL_NAME = "YCWTG/Qwen3-Coder-Next-int2-mixed-AutoRound"AUTO_MAX_TOKENS = TrueMANUAL_MAX_NEW_TOKENS = 128AUTO_MAX_TOKENS_RATIO = 1.5 HAS_CUDA = torch.cuda.is_available()# Read total VRAM once and use it to choose the default loading mode.GPU_TOTAL_MIB = torch.cuda.get_device_properties(0).total_memory // (1024 ** 2) if HAS_CUDA else 0# 32GB-class GPUs default to False; smaller GPUs default to True.ENABLE_CPU_OFFLOAD = HAS_CUDA and GPU_TOTAL_MIB < 32000MAX_MEMORY = {0: "18GiB", "cpu": "64GiB"} if ENABLE_CPU_OFFLOAD else {0: "22GiB", "cpu": "16GiB"} def get_input_device(model): # With device_map="auto", the first usable device may not be model.device. device_map = getattr(model, "hf_device_map", None) cpu_device = None if isinstance(device_map, dict): for loc in device_map.values(): if isinstance(loc, int): return torch.device(f"cuda:{loc}") if isinstance(loc, str): if loc.startswith("cuda"): return torch.device(loc) if loc.startswith("cpu"): cpu_device = torch.device("cpu") return cpu_device or next(model.parameters()).device def load_model(): print("Loading model...") tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True, use_fast=True) # Use EOS as PAD to avoid warnings for chat generation on models without a pad token. tokenizer.pad_token = tokenizer.pad_token or tokenizer.eos_token tokenizer.padding_side = "left" model_kwargs = { "pretrained_model_name_or_path": MODEL_NAME, "dtype": torch.bfloat16, "trust_remote_code": True, "low_cpu_mem_usage": True, "device_map": "auto" if HAS_CUDA else "cpu", } if HAS_CUDA: print(f"GPU total memory: {GPU_TOTAL_MIB} MiB") model_kwargs["max_memory"] = MAX_MEMORY if ENABLE_CPU_OFFLOAD: model_kwargs["offload_buffers"] = True print("CPU offload: ON") else: print("CPU offload: OFF (GPU preferred, small CPU spill enabled)") else: print("CUDA not available, running on CPU") try: model = AutoModelForCausalLM.from_pretrained(**model_kwargs) except RuntimeError as e: # Give a beginner-friendly hint instead of only showing the raw stack trace. if "out of memory" in str(e).lower(): print("\nCUDA OOM while loading the model.") print("Close other GPU programs, or set ENABLE_CPU_OFFLOAD = True and run again.") raise model.eval() return model, tokenizer def multiline_input(): print('User (type "END" on a single line to send, type "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 build_input_ids(tokenizer, messages, device): if getattr(tokenizer, "chat_template", None): # Preferred path for chat models: let the tokenizer build the prompt format. prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) else: # Generic fallback for tokenizers without a built-in chat template. prompt = "\n".join( [f"{'User' if m['role'] == 'user' else 'Assistant'}: {m['content']}" for m in messages] + ["Assistant:"] ) return tokenizer(prompt, return_tensors="pt")["input_ids"].to(device) def chat_loop(model, tokenizer): print("\n===== Chat Started =====\n") print(f"Auto max_tokens: {'ON' if AUTO_MAX_TOKENS else 'OFF'}") if not AUTO_MAX_TOKENS: print(f"Manual max_new_tokens: {MANUAL_MAX_NEW_TOKENS}") print( "Tip: Set ENABLE_CPU_OFFLOAD = False for a faster full-GPU attempt." if ENABLE_CPU_OFFLOAD else "Tip: If max_tokens is too large and you hit CUDA OOM, set ENABLE_CPU_OFFLOAD = True." ) messages = [] device = get_input_device(model) print(f"Input device: {device}") while True: user_text = multiline_input() if user_text is None: break messages.append({"role": "user", "content": user_text}) input_ids = build_input_ids(tokenizer, messages, device) prompt_tokens = int(input_ids.shape[-1]) # Auto mode scales output length with prompt length (1.5x by default). max_new_tokens = max(1, math.ceil(prompt_tokens * AUTO_MAX_TOKENS_RATIO)) if AUTO_MAX_TOKENS else int(MANUAL_MAX_NEW_TOKENS) print(f"Prompt tokens: {prompt_tokens}") print(f"max_new_tokens: {max_new_tokens}") try: with torch.inference_mode(): output_ids = model.generate( input_ids=input_ids, max_new_tokens=max_new_tokens, do_sample=True, temperature=1.0, top_p=0.95, top_k=40, use_cache=False, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, ) except RuntimeError as e: error_text = str(e).lower() if HAS_CUDA and ("cublas_status_alloc_failed" in error_text or "out of memory" in error_text): # Clear cached blocks so the next try starts from a cleaner CUDA state. torch.cuda.empty_cache() print("\nCUDA OOM during generation.") print("Set ENABLE_CPU_OFFLOAD = True, or disable AUTO_MAX_TOKENS and lower MANUAL_MAX_NEW_TOKENS.") messages.pop() continue raise reply_text = tokenizer.decode(output_ids[0, input_ids.shape[-1]:], skip_special_tokens=True) print(f"\nAssistant:\n{reply_text}\n") messages.append({"role": "assistant", "content": reply_text}) if __name__ == "__main__": model, tokenizer = load_model() chat_loop(model, tokenizer)from auto_round import AutoRound model_name = "Qwen/Qwen3-Coder-Next" # 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 if i in [3, 7, 11, 15, 19, 23, 27, 31, 35, 39, 43, 47]: # self_attn layers for proj in ["q_proj", "k_proj", "v_proj", "o_proj"]: layer_config[f"{prefix}.self_attn.{proj}"] = {"bits": 16} else: # linear_attn layers -> 16-bit for proj in ["in_proj_qkvz", "in_proj_ba", "out_proj"]: layer_config[f"{prefix}.linear_attn.{proj}"] = {"bits": 16} # MLP gate -> 16-bit layer_config[f"{prefix}.mlp.gate"] = {"bits": 16} # shared_expert_gate -> 16-bit (skipped) layer_config[f"{prefix}.mlp.shared_expert_gate"] = {"bits": 16} autoround = AutoRound( model_name, bits=2, # Default for experts group_size=128, sym=True, iters=1000, nsamples=512, lr=2e-3, layer_config=layer_config, low_gpu_mem_usage=True, enable_alg_ext=True)output_dir="~/model/YCWTG--Qwen3-Coder-Next-int2-mixed-AutoRound"autoround.quantize_and_save(output_dir,format="auto_round" )