Update README.md

README.md

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----
-library_name: transformers
-tags: []
----
 
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+## 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
 
-<!-- Provide a longer summary of what this model is. -->
+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.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+  - **Developed by:** ps2program
+  - **Model type:** Causal Language Model (LoRA-finetuned)
+  - **Language(s) (NLP):** English
+  - **License:** Apache-2.0
+  - **Finetuned from model:** `gemma3-270m`
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+### Model Sources
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+  - **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
 
-<!-- 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. -->
+This model is intended for direct use in applications requiring scientific text generation. Examples of its use include:
 
-[More Information Needed]
+  - Answering questions about scientific principles.
+  - Generating summaries of scientific papers or concepts.
+  - Assisting in educational contexts for students and researchers.
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+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
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+The model is **not intended** for:
 
-[More Information Needed]
+  - 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
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+  - **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
 
-<!-- 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.
+  - 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
 
-Use the code below 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.
 
-[More Information Needed]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
 
-## Training Details
+# Load the base model (you must have access to gemma3-270m)
+base_model_id = "google/gemma-3-270m"
+model_id = "ps2program/gemma3-270m-scisinstruct"
 
-### Training Data
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
 
-<!-- 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. -->
+# Load the LoRA adapters from your repo
+model = PeftModel.from_pretrained(base_model, model_id)
 
-[More Information Needed]
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_id)
 
-### Training Procedure
+# Prepare a scientific prompt
+prompt = "Explain the significance of CRISPR-Cas9 technology in genetic engineering."
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+# Generate the output
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=256,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.95
+)
 
-#### Preprocessing [optional]
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
-[More Information Needed]
+## Training Details
 
+### Training Data
 
-#### Training Hyperparameters
+The model was fine-tuned on the `zd21/SciInstruct` dataset, which contains high-quality, scientifically-grounded instruction-following examples.
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+### Training Procedure
 
-#### Speeds, Sizes, Times [optional]
+#### Preprocessing
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+The dataset was formatted into instruction-following prompts and tokenized using the `gemma3-270m` tokenizer.
 
-[More Information Needed]
+#### Training Hyperparameters
 
-## Evaluation
+  - **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`
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+## Evaluation
 
 ### 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]
+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
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+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
 
-<!-- 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]
+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.
 
-### Compute Infrastructure
+  - **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]
 
-[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. -->
+## Citation
 
 **BibTeX:**
 
-[More Information Needed]
+```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:**
 
-[More Information Needed]
-
-## 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]
+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 [optional]
+## Model Card Authors
 
-[More Information Needed]
+ps2program
 
 ## Model Card Contact
 
-[More Information Needed]
+[Your preferred contact method or email address]