radical-ai
/

MATRIX-PT

@@ -13,200 +13,150 @@ language:
   - en
 ---
-# Model Card for MATRIX-PT
-<!-- Provide a quick summary of what the model is/does. -->
-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, including theoretical reasoning, scientific problem solving, and multimodal reasoning over experimental images.
-## Model Details
-<!-- Provide a longer summary of what this model is. -->
 ### Model Description
-- **Developed by:** Radical AI
-- **Model type:** LoRA adapter (PEFT) for a multimodal transformer (Qwen2-VL-7B)
-- **Base model:** `Qwen/Qwen2-VL-7B`
-- **Language(s):** English
-- **License:** mit
 - **Finetuned from model:** `Qwen/Qwen2-VL-7B`
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 ## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 ### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
 ### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
 ## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
 ### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 ## How to Get Started with the Model
-Use the code below to get started with the model.
 ### Install
 ```bash
 pip install -U transformers peft accelerate torch
 ```
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
 ## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
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-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
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-## Model Card Contact
-[More Information Needed]
-### Framework versions
-- PEFT 0.18.0

   - en
 ---
+# 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](https://huggingface.co/datasets/radical-ai/MATRIX)), which is used to evaluate reasoning across text- and image-based materials science tasks.
+---
+## 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
+- **Paper:** *link to preprint*
+- **Benchmark:** https://huggingface.co/datasets/radical-ai/MATRIX
+---
 ## 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
+---
 ## 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
+---
 ## How to Get Started with the Model
 ### Install
 ```bash
 pip install -U transformers peft accelerate torch
 ```
+### Load the Adapter
+``` python
+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
+For evaluation details, see the [MATRIX dataset](https://huggingface.co/datasets/radical-ai/MATRIX) card and accompanying paper.
+### 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.
+## Citation
+If you use this model or the MATRIX benchmark, please cite the accompanying paper:
+[MATRIX: A Multimodal Benchmark and Post-Training Framework for Materials Science]()
+### Bibtex
+```
+```
+### Framework Versions
+PEFT 0.18.0