MATRIX-PT

MATRIX-PT is a parameter-efficient LoRA adapter released by Radical AI for Qwen/Qwen2-VL-7B. It is designed to study post-training adaptations for materials science tasks, with a focus on theoretical reasoning, scientific problem solving, and multimodal reasoning over experimental images.

This model is released alongside the MATRIX benchmark (dataset link), which is used to evaluate reasoning across text- and image-based materials science tasks.

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Model Details

Model Description

• Developed by: Radical AI
• Model type: LoRA adapter (PEFT) for a multimodal transformer
• Base model: Qwen/Qwen2-VL-7B
• Language(s): English
• License: Apache-2.0 (adapter); base model license applies to Qwen/Qwen2-VL-7B
• Finetuned from model: Qwen/Qwen2-VL-7B

MATRIX-PT modifies the base model through lightweight post-training to better surface domain-relevant reasoning patterns in materials science. The adapter primarily affects inference-time behavior, improving the model’s ability to reason about structured scientific concepts and experimental imagery without altering the underlying base weights.

Model Sources

• Repository: https://huggingface.co/radical-ai/MATRIX-PT
• Benchmark: https://huggingface.co/datasets/radical-ai/MATRIX

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Uses

Direct Use

MATRIX-PT is intended for:

• Evaluating multimodal reasoning in materials science
• Studying post-training effects on scientific reasoning behavior
• Benchmarking model performance on theory-driven and experiment-driven tasks using MATRIX

The adapter can be loaded on top of Qwen/Qwen2-VL-7B using PEFT without modifying the base model weights.

Downstream Use

The adapter may be used as a starting point for:

• Further domain-specific fine-tuning
• Diagnostic studies of reasoning behavior in scientific models
• Comparative evaluation against other multimodal or domain-adapted models

Out-of-Scope Use

MATRIX-PT is not intended for:

• General-purpose conversational use
• High-stakes decision making (e.g., medical, legal, industrial control)
• Deployment without human oversight in safety-critical settings

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Bias, Risks, and Limitations

• MATRIX-PT inherits limitations and biases from the base model, including potential hallucinations and incorrect reasoning.
• The adapter is trained and evaluated on a focused materials science benchmark and may not generalize outside this domain.
• Performance improvements are task- and prompt-dependent and should not be interpreted as broad scientific understanding.
• As with most LLMs/VLMs, the model may produce plausible-sounding but incorrect explanations.

Recommendations

Users should:

• Treat outputs as assistive rather than authoritative
• Validate results against domain expertise or ground truth
• Use MATRIX-PT primarily for evaluation, analysis, and research purposes

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How to Get Started with the Model

Install

pip install -U transformers peft accelerate torch

Load the Adapter

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base_id = "Qwen/Qwen2-VL-7B"
adapter_id = "radical-ai/MATRIX-PT"

tokenizer = AutoTokenizer.from_pretrained(base_id, trust_remote_code=True)

base_model = AutoModelForCausalLM.from_pretrained(
    base_id,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
)

model = PeftModel.from_pretrained(base_model, adapter_id)
model.eval()

Training Details

Training Data

The adapter was trained using a curated materials science dataset emphasizing:

• Foundational theory questions
• Research-level reasoning
• Hypothesis generation
• Multimodal reasoning over experimental imagery

Training Procedure

• Method: LoRA (parameter-efficient fine-tuning)
• Objective: Improve accessibility of materials science-relevant reasoning patterns during inference
• Training regime: Mixed precision (bf16)

Evaluation

Testing Data

MATRIX-PT is benchmarked on the MATRIX dataset, which consists of both textual and visual reasoning tasks in materials science. Evaluation compares the adapted model against the base Qwen/Qwen2-VL-7B model under identical prompting and decoding settings.

Metrics

• Task accuracy
• Reasoning consistency across related prompts
• Qualitative error analysis (see accompanying paper)

Results

Across MATRIX tasks, MATRIX-PT demonstrates improved performance relative to the base model, particularly on:

• Theory-driven reasoning questions
• Structured scientific problem solving
• Interpretation of experimental images

These improvements primarily manifest at inference time, highlighting the role of post-training in shaping reasoning accessibility rather than training-time memorization alone.

### Framework Versions

PEFT 0.18.0