Update README.md

README.md

@@ -9,35 +9,41 @@ tags:
 - transformers
 - trl
 - unsloth
+- vcet
+- domain-specific
+license: apache-2.0
+metrics:
+- accuracy
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
 
+# Model Card for gemma-270-it-vcet-lora
 
+<!-- Provide a quick summary of what the model is/does. -->
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+This model is a domain-specific conversational AI fine-tuned on custom data related to VCET College, Madurai.
+Built on top of unsloth/gemma-3-270m-it-unsloth-bnb-4bit, it uses LoRA and PEFT for efficient adaptation. 
+The model is designed to answer queries about campus life, academics, departments, events, and administrative processes at VCET.
 
 
 
-- **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:** SandeepCodez.
+- **Funded by [optional]:** Self-funded.
+- **Shared by [optional]:** SandeepCodez
+- **Model type:** Causal Language Model (Text Generation).
+- **Language(s) (NLP):** English (with contextual Tamil understanding).
+- **License:** Apache 2.0.
+- **Finetuned from model [optional]:** unsloth/gemma-3-270m-it-unsloth-bnb-4bit
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
+- **Repository:** https://github.com/SandeepCodez
 - **Paper [optional]:** [More Information Needed]
 - **Demo [optional]:** [More Information Needed]
 
@@ -47,45 +53,84 @@ tags:
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Answering VCET-related questions
+
+Assisting students with academic and campus queries
+
+Automating college FAQs
+
+Supporting chatbot integration for VCET platforms
 
 [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 -->
+Integration into college ERP systems
+
+Enhancing virtual assistants for student support
+
+Embedding in mobile apps or websites
 
 [More Information Needed]
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+General-purpose text generation outside VCET context
+
+Legal, medical, or financial advice
+
+High-stakes decision-making without human oversight
 
 [More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+May reflect institutional bias from VCET sources
+
+Limited generalization outside VCET domain
+
+Not suitable for sensitive or critical applications
 
 [More Information Needed]
 
 ### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+Use in supervised environments
+
+Periodic updates to dataset recommended
+
+Human validation for factual accuracy advised
 
 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.
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "SandeepCodez/gemma-270-it-vcet-lora"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+inputs = tokenizer("What are the placement statistics for VCET Madurai?", return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0]))
 
-[More Information Needed]
 
 ## 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. -->
+Custom dataset created by the developer, including:
+
+VCET brochures
+
+Departmental documents
+
+Student interviews
+
+Campus FAQs
+
+Event archives
 
 [More Information Needed]
 
@@ -95,16 +140,32 @@ Use the code below to get started with the model.
 
 #### Preprocessing [optional]
 
-[More Information Needed]
+Cleaned and structured into JSONL format
+
+Tokenized using Gemma tokenizer
+
+Filtered for relevance and clarity
 
 
 #### 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 -->
+Training regime: bf16 mixed precision
+
+Epochs: 3
+
+Batch Size: 16
+
+Learning Rate: 2e-4
+
+Frameworks: PEFT 0.17.1, TRL, Unsloth
 
 #### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+Training Time: ~3 hours
+
+Dataset Size: ~10,000 samples
+
+Model Size: 270M parameters
 
 [More Information Needed]