Update README.md

README.md

@@ -1,11 +1,18 @@
 ---
 library_name: transformers
-tags: []
+license: mit
+language:
+- en
+metrics:
+- rouge
+base_model:
+- microsoft/Phi-3-mini-4k-instruct
+pipeline_tag: text-generation
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+A lightweight LoRA-adapter fine-tune of microsoft/Phi-3-mini-4k-instruct for turning structured lab contexts + observations into executable Python code that performs the target calculations (e.g., mechanics, fluids, vibrations, basic circuits, titrations). Trained with QLoRA in 4-bit, this model is intended as an assistive code generator for STEM lab writeups and teaching demos—not as a certified calculator for safety-critical engineering.
 
 
 
@@ -17,13 +24,11 @@ tags: []
 
 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:** [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]
+- **Developed by:** [Barghav777]
+- **Model type:** Causal decoder LM (instruction-tuned) + LoRA adapter
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model [optional]:** microsoft/Phi-3-mini-4k-instruct
 
 ### Model Sources [optional]
 
@@ -39,34 +44,41 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+- Generate readable Python code to compute derived quantities from lab observations (e.g., average g via pendulum, Coriolis acceleration, Ohm’s law resistances, radius of gyration, Reynolds number).
 
-[More Information Needed]
+- Produce calculation pipelines with minimal plotting/printing that are easy to copy-paste and run in a notebook.
 
 ### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+- Course assistants or lab-prep tools that auto-draft calculation code for intro undergrad physics/mech/fluids/EE labs.
 
-[More Information Needed]
+- Auto-checkers that compare student code vs. a reference implementation (with appropriate guardrails).
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+- Any safety-critical design decisions (structural, medical, chemical process control).
 
-[More Information Needed]
+- High-stakes computation without human verification.
+
+- Domains far outside the training distribution (e.g., NLP preprocessing pipelines, advanced control systems, large-scale simulation frameworks).
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+- Small dataset (37 train / 6 eval) → plausible overfitting; brittle generalization to unseen experiment formats.
 
-[More Information Needed]
+- Formula misuse risk: The model may pick incorrect constants/units or silently use wrong equations.
+
+- Overconfidence: Generated code may “look right” while being numerically off or unit-inconsistent.
+
+- JSON brittleness: If OBSERVATIONS keys/units differ from training patterns, the code may break.
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+- Always review formulas and units; add assertions/unit conversions in downstream systems.
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+- Run generated code with test observations and compare against hand calculations.
 
+- For deployment, wrap outputs with explanations and references to the formulas used.
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.