| ## library\_name: transformers tags: [scientific, instruction-following, gemma, lora, gemma3-270m, science] | |
| # Model Card for ps2program/gemma3-270m-scisinstruct | |
| This is a **LoRA-fine-tuned version of Gemma 3 (270M)**, specialized for **scientific instruction-following and reasoning tasks**. The model has been trained on the `zd21/SciInstruct` dataset to excel at generating explanations, summaries, and scientific reasoning outputs in response to natural language prompts. | |
| ## Model Details | |
| ### Model Description | |
| This model extends the Google-developed Gemma 3 base language model using LoRA (Low-Rank Adaptation) adapters. This technique allows for efficient fine-tuning on a specific task without modifying the entire model. The resulting model is designed to handle a variety of scientific questions and instructions, making it a valuable tool for academic and research-oriented applications. | |
| - **Developed by:** ps2program | |
| - **Model type:** Causal Language Model (LoRA-finetuned) | |
| - **Language(s) (NLP):** English | |
| - **License:** Apache-2.0 | |
| - **Finetuned from model:** `gemma3-270m` | |
| ### Model Sources | |
| - **Repository:** [https://huggingface.co/ps2program/gemma3-270m-scisinstruct](https://huggingface.co/ps2program/gemma3-270m-scisinstruct) | |
| - **Dataset Card:** [https://huggingface.co/datasets/zd21/SciInstruct](https://huggingface.co/datasets/zd21/SciInstruct) | |
| ## Uses | |
| ### Direct Use | |
| This model is intended for direct use in applications requiring scientific text generation. Examples of its use include: | |
| - Answering questions about scientific principles. | |
| - Generating summaries of scientific papers or concepts. | |
| - Assisting in educational contexts for students and researchers. | |
| ### Downstream Use | |
| This model can serve as a base for further fine-tuning on highly specialized, domain-specific scientific corpora (e.g., specific fields like biochemistry or astrophysics) to improve performance on those particular tasks. | |
| ### Out-of-Scope Use | |
| The model is **not intended** for: | |
| - General-purpose conversation or casual chat. | |
| - Providing medical, legal, or financial advice. | |
| - Generating content in non-scientific domains where it may produce inaccurate or nonsensical outputs. | |
| - Applications where factual accuracy is critical without human verification. | |
| ## Bias, Risks, and Limitations | |
| - **Factual Inaccuracies:** The model may generate factually incorrect or outdated information. Users should always verify outputs, especially in academic or research contexts. | |
| - **Data Bias:** The model's performance and outputs are limited by the quality and content of its training data (`SciInstruct`). It may reflect any biases present in the original dataset. | |
| - **Limited Scope:** The model is specialized for scientific reasoning and may perform poorly on tasks outside of this domain. | |
| ### Recommendations | |
| - Always verify the generated scientific content with reliable sources. | |
| - Do not use the model for high-stakes decision-making. | |
| - Be aware of the model's limitations and potential for generating incorrect information. | |
| ## How to Get Started with the Model | |
| To use this model, you'll need to load the base `gemma3-270m` model first, and then load the LoRA adapters on top of it. | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from peft import PeftModel | |
| import torch | |
| # Load the base model (you must have access to gemma3-270m) | |
| base_model_id = "google/gemma-3-270m" | |
| model_id = "ps2program/gemma3-270m-scisinstruct" | |
| base_model = AutoModelForCausalLM.from_pretrained( | |
| base_model_id, | |
| torch_dtype=torch.bfloat16, | |
| device_map="auto" | |
| ) | |
| # Load the LoRA adapters from your repo | |
| model = PeftModel.from_pretrained(base_model, model_id) | |
| # Load the tokenizer | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| # Prepare a scientific prompt | |
| prompt = "Explain the significance of CRISPR-Cas9 technology in genetic engineering." | |
| inputs = tokenizer(prompt, return_tensors="pt").to("cuda") | |
| # Generate the output | |
| outputs = model.generate( | |
| **inputs, | |
| max_new_tokens=256, | |
| do_sample=True, | |
| temperature=0.7, | |
| top_p=0.95 | |
| ) | |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True)) | |
| ``` | |
| ## Training Details | |
| ### Training Data | |
| The model was fine-tuned on the `zd21/SciInstruct` dataset, which contains high-quality, scientifically-grounded instruction-following examples. | |
| ### Training Procedure | |
| #### Preprocessing | |
| The dataset was formatted into instruction-following prompts and tokenized using the `gemma3-270m` tokenizer. | |
| #### Training Hyperparameters | |
| - **Training regime:** Mixed precision (bfloat16) | |
| - **LoRA parameters:** `r=8`, `lora_alpha=16` | |
| - **Optimizer:** `adamw_torch_fused` | |
| - **Learning rate:** `2e-5` | |
| - **Batch size:** `4` | |
| - **Epochs:** `5` | |
| - **Gradient accumulation steps:** `4` | |
| ## Evaluation | |
| ### Testing Data, Factors & Metrics | |
| The model's performance was evaluated qualitatively by observing its ability to generate coherent and scientifically accurate responses to a diverse set of prompts. Standard metrics were not used in this initial fine-tuning. | |
| ### Results | |
| The model demonstrates an improved ability to follow scientific instructions compared to the base `gemma3-270m` model. It can provide well-structured explanations and summaries. | |
| ## Environmental Impact | |
| Carbon emissions were not calculated for this fine-tuning process. However, as a small-parameter model fine-tuned using LoRA, the training was computationally efficient and had a significantly lower environmental impact than training a large model from scratch. | |
| - **Hardware Type:** (e.g., 1x NVIDIA A100 GPU) | |
| - **Hours used:** (e.g., \~2 hours) | |
| - **Cloud Provider:** (e.g., Google Cloud, AWS, etc.) | |
| - **Compute Region:** (e.g., us-east-1) | |
| - **Carbon Emitted:** [More Information Needed] | |
| ## Citation | |
| **BibTeX:** | |
| ```bibtex | |
| @misc{ps2program2025gemma3, | |
| title={Gemma 3 270M SciInstruct LoRA}, | |
| author={ps2program}, | |
| year={2025}, | |
| howpublished={\url{https://huggingface.co/ps2program/gemma3-270m-scisinstruct}}, | |
| note={LoRA fine-tuning of Gemma 3 270M on the SciInstruct dataset.} | |
| } | |
| ``` | |
| **APA:** | |
| ps2program. (2025). *Gemma 3 270M SciInstruct LoRA*. Hugging Face Model Hub. [https://huggingface.co/ps2program/gemma3-270m-scisinstruct](https://huggingface.co/ps2program/gemma3-270m-scisinstruct) | |
| ## Model Card Authors | |
| ps2program | |
| ## Model Card Contact | |
| [Your preferred contact method or email address] |