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library_name: transformers
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tags: []
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---
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- 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. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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library_name: transformers
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tags: []
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---
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# Cephalo-Gemma-3-4b
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```python
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import torch
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from transformers import AutoProcessor, Gemma3ForConditionalGeneration
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from transformers.image_utils import load_image
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from PIL import Image as PILImage
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ckpt = "lamm-mit/Cephalo-Gemma-3-4b-it-04-15-2025"
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model = Gemma3ForConditionalGeneration.from_pretrained(
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ckpt, device_map="auto", torch_dtype=torch.bfloat16,
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)
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processor = AutoProcessor.from_pretrained(ckpt)
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image=PILImage.open(f'./spiderweb.png').convert("RGB")
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messages = [
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{
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"role": "system",
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"content": [
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{"type": "text", "text": "You are a materials scientist."}
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],
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"role": "user",
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"content": [
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{"type": "image", "image": image},
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{"type": "text", "text": "What does this image show? Provide a detailed analysis."}
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]
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}
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]
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inputs = processor.apply_chat_template(
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messages, add_generation_prompt=True, tokenize=True,
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return_dict=True, return_tensors="pt"
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).to(model.device)
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input_len = inputs["input_ids"].shape[-1]
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generation = model.generate(**inputs, max_new_tokens=512, do_sample=False)
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generation = generation[0][input_len:]
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decoded = processor.decode(generation, skip_special_tokens=True)
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print(decoded)
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```
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Output:
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```raw
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The image shows a comparison between a 3D model of a structure and its physical 3D printed counterpart.
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The top part of the image displays a 3D model of a structure, which is a complex geometric design with multiple interconnected lines and angles. The model is likely created using computer-aided design (CAD) software, which allows for precise and detailed representation of the structure.
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The bottom part of the image shows the physical 3D printed version of the same structure. The printed object is a tangible representation of the CAD model, with the same geometric design and intricate details. The printed object is placed on a surface, which could be a table or a platform, and is illuminated to highlight its three-dimensional form.
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The comparison between the 3D model and the physical 3D printed object demonstrates the accuracy and fidelity of the 3D printing process. The printed object closely resembles the CAD model, indicating that the 3D printing technology can accurately reproduce complex geometric designs.
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The results shown in the image highlight the potential of 3D printing for creating complex and intricate structures with high precision and accuracy. This technology has various applications in fields such as manufacturing, engineering, and design, where the ability to create precise and detailed objects is crucial.
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```
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