Intel/Qwen3-235B-A22B-Thinking-2507-int4-mixed-AutoRound

Model Details

This model is a mixed int4 model with group_size 64 and symmetric quantization of Qwen/Qwen3-235B-A22B-Thinking-2507 generated by intel/auto-round via RTN (no algorithm tuning). Non expert layers are fall back to 8 bits and group_size 128

Please follow the license of the original model.

How To Use

INT4 Inference on CPU/Intel GPU/CUDA

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Intel/Qwen3-235B-A22B-Thinking-2507-int4-mixed-AutoRound"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()

# parsing thinking content
try:
    # rindex finding 151668 (</think>)
    index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
    index = 0

thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")

print("thinking content:", thinking_content) # no opening <think> tag
print("content:", content)

"""
thinking content: Okay, the user asked for a short introduction to large language models. Let me start by understanding their request. They want something concise, so I need to keep it brief but informative.

First, I should define what an LLM is. They're AI systems trained on massive text data. Mentioning key capabilities like text generation, translation, and answering questions would cover the basics.

Wait, the user might not know technical terms. I should avoid jargon. Terms like "neural networks" might be too much. Maybe say "advanced AI systems" instead.

Also, consider why they're asking. Could be a student, a professional looking to get into AI, or just curious. Since they want it short, they probably need a quick overview without deep details.

I should highlight real-world applications—chatbots, writing assistance—to make it relatable. Emphasize that they predict text based on patterns, not understanding like humans. That's important to avoid misconceptions.

Check if there are common misunderstandings. People often think LLMs "know" things, but they're pattern matchers. Clarifying that they don't have true understanding or consciousness is crucial.

Structure: Start with a simple definition, key features, how they work (training on data), what they can do, and a note on limitations. Keep each part to one or two sentences.

Make sure it's neutral—mention both capabilities and limitations. Avoid hype; be factual.

Length: Aim for 3-4 short paragraphs. User said "short," so under 200 words probably.

Double-check examples: ChatGPT, Gemini are well-known. Mentioning them helps.

Finally, end with why they matter—transforming how we interact with tech. That ties it together nicely.
</think>
content: Here's a concise introduction to **Large Language Models (LLMs)**:

**Large Language Models (LLMs)** are advanced artificial intelligence systems trained on *massive* amounts of text data (like books, websites, and articles). They learn patterns, grammar, facts, and reasoning abilities from this data, enabling them to **generate human-like text**, **answer questions**, **translate languages**, **summarize content**, **write creatively**, and even **code**.

**Key things to know:**
1.  **"Large" = Scale:** They have billions (or trillions) of parameters (internal settings learned during training), allowing them to handle complex language tasks.
2.  **Predict

"""

Generate the model

Here is the sample command to reproduce the model

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from auto_round import AutoRound

model_name = "Qwen/Qwen3-235B-A22B-Thinking-2507"

model = AutoModelForCausalLM.from_pretrained(model_name,
                                             device_map="cpu", torch_dtype="auto")

tokenizer = AutoTokenizer.from_pretrained(model_name)

layer_config = {}
for n, m in model.named_modules():
    if isinstance(m, torch.nn.Linear) and (not "expert" in n or "shared_experts" in n) and n != "lm_head":
        layer_config[n] = {"bits": 8, "group_size": 128}

autoround = AutoRound(model, tokenizer, iters=0, group_size=64, layer_config=layer_config)
autoround.quantize_and_save("./Qwen3-235B-A22B-Thinking-2507-int4-mixed")

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 link

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 github