microsoft/X-Reasoner-7B

Introduction

We introduce X-Reasoner, a vision-language model posttrained solely on general-domain text for generalizable reasoning, using a twostage approach: an initial supervised fine-tuning phase with distilled long chainof-thoughts, followed by reinforcement learning with verifiable rewards. Experiments show that X-Reasoner successfully transfers reasoning capabilities to both multimodal and out-of-domain settings, outperforming existing state-of-theart models trained with in-domain and multimodal data across various general and medical benchmarks. More details can be found in the paper: X-Reasoner: Towards Generalizable Reasoning Across Modalities and Domains

Requirements

We recommend installing the transformers version used in our experiments and other dependencies with this command:

pip install transformers==4.57.1 accelerate==1.12.0 torchvision==0.24.1 qwen-vl-utils==0.0.14

Quickstart

Below, we provide a some examples to show how to use X-Reasoner with 🤗 Transformers or vLLM.

Inference with HF Transformers 🤗 Here we show a code snippet to show you how chat with X-Reasoner using `transformers` and `qwen_vl_utils`:

import torch
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info

# default: Load the model on the available device(s)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "microsoft/X-Reasoner-7B", dtype=torch.bfloat16, device_map="auto"
)

# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
#     "microsoft/X-Reasoner",
#     dtype=torch.bfloat16,
#     attn_implementation="flash_attention_2",
#     device_map="auto",
# )

# You can set min_pixels and max_pixels according to your needs.
min_pixels = 262144
max_pixels = 262144
processor = AutoProcessor.from_pretrained("microsoft/X-Reasoner-7B", min_pixels=min_pixels, max_pixels=max_pixels)

# Multiple Choice Query
messages = [
    {
        "role": "user",
        "content": [
           
            {"type": "text", "text": "You should provide your thoughts within <think> </think> tags, then answer with just one of the options below within <answer> </answer> tags (For example, if the question is \n'Is the earth flat?\n A: Yes \nB: No', you should answer with <think>...</think> <answer>B: No</answer>). \nHere is the question:"},
             {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"type": "text", "text": "Is there a dog in the image? A. Yes B. No"},
        ],
    }
]

# Preparation for inference
text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)

        
inputs = inputs.to(device="cuda")

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=4000)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

Inference with vLLM

Here we show an example of how to use X-Reasoner-7B with vLLM (tested with vLLM==0.11.2 and transformers==4.57.1):

from vllm import LLM, SamplingParams
from transformers import AutoProcessor

min_pixels = 262144
max_pixels = 262144
processor = AutoProcessor.from_pretrained("microsoft/X-Reasoner-7B", min_pixels=min_pixels, max_pixels=max_pixels)

llm = LLM(
    model="microsoft/X-Reasoner-7B",
    trust_remote_code=True,
    dtype="bfloat16",
    max_model_len=8192,
    tensor_parallel_size=4,
    gpu_memory_utilization=0.8,
    limit_mm_per_prompt={"image": 1}
)

# Set up sampling parameters
sampling_params = SamplingParams(
    temperature=0.6,
    max_tokens=4000,
)

image_data = []



# Multiple Choice Query
image_data = ['https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg']
messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": image_data[0],
            },
            {"type": "text", "text": "You should provide your thoughts within <think> </think> tags, then answer with just one of the options below within <answer> </answer> tags (For example, if the question is \n'Is the earth flat?\n A: Yes \nB: No', you should answer with <think>...</think> <answer>B: No</answer>). \nHere is the question: Is there a dog in the picture? A: Yes B: No"},
        ],
    }
]

prompt = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True)

if image_data:
    mm_prompt = {
        "prompt": prompt,
        "multi_modal_data": {"image": image_data}
    }
else:
    mm_prompt = {"prompt": prompt}

# Generate response
outputs = llm.generate([mm_prompt], sampling_params)

# Print the generated response
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt}")
    print(f"Generated text: {generated_text}")
    print("-" * 50)

Known Issues

In case the model generates non-stopping reasoning trace, we add </think> as a stop token to the assistant output and re-run to generate the final answer.

Citation

If you find our work helpful, feel free to give us a cite.