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README.md

@@ -1,199 +1,292 @@
 ---
+license: gemma
+base_model: google/functiongemma-270m-it
+tags:
+- text-classification
+- domain-classification
+- function-calling
+- peft
+- lora
+- gemma
+- functiongemma
+datasets:
+- custom
+language:
+- en
+metrics:
+- accuracy
+- f1
 library_name: transformers
-tags: []
+pipeline_tag: text-classification
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# FunctionGemma Domain Classifier
 
+Fine-tuned **FunctionGemma-270M** for multi-domain query classification using LoRA.
 
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-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]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## 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. -->
-
-[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 -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### 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.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+- **Base Model:** [google/functiongemma-270m-it](https://huggingface.co/google/functiongemma-270m-it)
+- **Model Size:** 270M parameters (540MB)
+- **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
+- **Trainable Parameters:** ~7.6M (2.75%)
+- **Training Time:** 23.3 minutes
+- **Hardware:** GPU (memory optimized for <5GB VRAM)
+
+## Performance
+
+```
+Accuracy: 95.51%
+F1 Score (Weighted): 0.96
+F1 Score (Macro): 0.88
+Training Loss: 0.3
+```
+
+## Supported Domains (17)
+
+1. ambiguous
+2. api_generation
+3. business
+4. coding
+5. creative_content
+6. data_analysis
+7. education
+8. general_knowledge
+9. geography
+10. history
+11. law
+12. literature
+13. mathematics
+14. medicine
+15. science
+16. sensitive
+17. technology
+
+## Use Cases
+
+- **Query Routing:** Route user queries to specialized models/services
+- **Content Classification:** Categorize text by domain
+- **Multi-domain Detection:** Identify queries spanning multiple domains
+- **Intent Analysis:** Understand query context and domain
+
+## Quick Start
+
+### Installation
+
+```bash
+pip install transformers peft torch
+```
+
+### Inference
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+import json
+
+# Load model
+base_model = AutoModelForCausalLM.from_pretrained(
+    "google/functiongemma-270m-it",
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+model = PeftModel.from_pretrained(base_model, "ovinduG/functiongemma-domain-classifier")
+tokenizer = AutoTokenizer.from_pretrained("ovinduG/functiongemma-domain-classifier")
+
+# Classify a query
+def classify(text):
+    # Define function schema
+    function_def = {
+        "type": "function",
+        "function": {
+            "name": "classify_query_domain",
+            "description": "Classify query into domains",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "primary_domain": {"type": "string"},
+                    "primary_confidence": {"type": "number"},
+                    "is_multi_domain": {"type": "boolean"},
+                    "secondary_domains": {"type": "array"}
+                }
+            }
+        }
+    }
+    
+    messages = [
+        {"role": "developer", "content": "You are a model that can do function calling"},
+        {"role": "user", "content": text}
+    ]
+    
+    inputs = tokenizer.apply_chat_template(
+        messages,
+        tools=[function_def],
+        add_generation_prompt=True,
+        return_dict=True,
+        return_tensors="pt"
+    ).to(model.device)
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=150,
+            do_sample=False,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    
+    response = tokenizer.decode(
+        outputs[0][inputs["input_ids"].shape[-1]:],
+        skip_special_tokens=True
+    )
+    
+    # Parse function call
+    if "{" in response:
+        start = response.find("{")")
+        end = response.rfind("}") + 1
+        return json.loads(response[start:end])
+    
+    return {"error": "Failed to parse response"}
+
+# Example
+result = classify("Write a Python function to calculate fibonacci numbers")
+print(json.dumps(result, indent=2))
+```
+
+### Example Output
+
+```json
+{
+  "primary_domain": "coding",
+  "primary_confidence": 0.95,
+  "is_multi_domain": false,
+  "secondary_domains": []
+}
+```
+
+### Multi-Domain Example
+
+```python
+result = classify("Build an ML model to predict customer churn and create REST API endpoints")
+print(json.dumps(result, indent=2))
+```
+
+```json
+{
+  "primary_domain": "data_analysis",
+  "primary_confidence": 0.85,
+  "is_multi_domain": true,
+  "secondary_domains": [
+    {
+      "domain": "api_generation",
+      "confidence": 0.75
+    }
+  ]
+}
+```
 
 ## 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. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### 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 -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
+### Dataset
+
+- **Total Samples:** 5,046
+- **Training Samples:** 3,666
+- **Validation Samples:** 690
+- **Test Samples:** 690
+- **Multi-domain Queries:** 546 (10.8%)
+
+### Training Configuration
+
+```python
+# LoRA Configuration
+r = 32
+lora_alpha = 64
+lora_dropout = 0.05
+target_modules = ['q_proj', 'v_proj', 'k_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj']
+
+# Training Configuration  
+num_epochs = 5
+batch_size = 4
+gradient_accumulation_steps = 8
+learning_rate = 0.0003
+max_length = 1024
+optimizer = "adamw_8bit"  # Memory optimized
+```
+
+### Memory Optimization
+
+This model was trained with memory optimizations to run on GPUs with <5GB VRAM:
+
+- **8-bit Optimizer:** Reduces optimizer memory by 50%
+- **Gradient Checkpointing:** Trades compute for memory
+- **Smaller Batches:** 4 samples per batch with gradient accumulation
+- **Shorter Sequences:** 1024 tokens max (vs 2048)
+
+**Total VRAM Usage:** ~4GB (vs ~40GB without optimization)
+
+## Performance by Domain
+
+| Domain | Precision | Recall | F1-Score | Support |
+|--------|-----------|--------|----------|---------|
+| ambiguous | 0.98 | 1.00 | 0.99 | 45 |
+| api_generation | 0.98 | 1.00 | 0.99 | 45 |
+| business | 0.98 | 0.93 | 0.95 | 44 |
+| coding | 0.98 | 0.96 | 0.97 | 48 |
+| creative_content | 0.90 | 1.00 | 0.95 | 45 |
+| data_analysis | 0.96 | 0.98 | 0.97 | 46 |
+| education | 0.98 | 0.96 | 0.97 | 45 |
+| general_knowledge | 0.76 | 0.84 | 0.80 | 45 |
+| law | 0.98 | 0.94 | 0.96 | 49 |
+| literature | 1.00 | 0.93 | 0.97 | 45 |
+| mathematics | 1.00 | 1.00 | 1.00 | 47 |
+| medicine | 0.98 | 0.89 | 0.93 | 46 |
+| science | 1.00 | 0.98 | 0.99 | 47 |
+| sensitive | 0.92 | 1.00 | 0.96 | 45 |
+| technology | 1.00 | 0.93 | 0.97 | 46 |
+
+**Overall Accuracy:** 95.51%
+
+## Advantages
+
+- ✅ **Tiny Size:** 270M parameters (14x smaller than Phi-3)
+- ✅ **Fast Inference:** 0.3s on CPU, 0.08s on GPU
+- ✅ **Low Memory:** Runs on 4GB VRAM
+- ✅ **High Accuracy:** 95.51% (competitive with larger models)
+- ✅ **Multi-domain:** Detects queries spanning multiple domains
+- ✅ **Function Calling:** Built-in structured output
+- ✅ **Mobile-Ready:** Can deploy on smartphones
+
+## Limitations
+
+- Trained on English queries only
+- Performance varies by domain (see table above)
+- May struggle with highly ambiguous queries
+- Limited to 17 pre-defined domains
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{functiongemma-domain-classifier,
+  author = {ovinduG},
+  title = {FunctionGemma Domain Classifier},
+  year = {2024},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/ovinduG/functiongemma-domain-classifier}}
+}
+```
+
+## License
+
+This model is based on [FunctionGemma](https://huggingface.co/google/functiongemma-270m-it) and follows the same [Gemma License](https://ai.google.dev/gemma/terms).
+
+## Acknowledgments
+
+- **Base Model:** Google's FunctionGemma-270M
+- **Training Framework:** HuggingFace Transformers + PEFT
+- **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
 
-<!-- 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]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## 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]
-
-### Compute Infrastructure
-
-[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. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
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-
-## Glossary [optional]
-
-<!-- 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 [optional]
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-
-## Model Card Authors [optional]
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-
-## Model Card Contact
+---
 
-[More Information Needed]
+Built with ❤️ using FunctionGemma