sarvkk
/

gemma-event-parser

@@ -3,205 +3,204 @@ base_model: google/gemma-2-2b-it
 library_name: peft
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:google/gemma-2-2b-it
 - lora
-- transformers
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **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]
 ## 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
-<!-- 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:**
-[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]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]
-### Framework versions
-- PEFT 0.18.1

 library_name: peft
 pipeline_tag: text-generation
 tags:
 - lora
+- function-calling
+- sports
+- event-parsing
+- natural-language-processing
+license: gemma
+language:
+- en
 ---
+# Gemma 2B Event Parser - Sports Event Function Calling
+A fine-tuned LoRA adapter for Gemma 2B that converts natural language descriptions into structured JSON for creating sports events.
+## Model Description
+This model takes casual text like **"I want to play soccer this week Friday 4 PM @ Central Park"** and converts it into a properly formatted `CreateEventRequest` JSON object for backend API consumption.
+**Base Model:** `google/gemma-2-2b-it`
+**Fine-tuning Method:** LoRA (Low-Rank Adaptation)
+**Training Framework:** Transformers + PEFT
+**Primary Use Case:** Natural language to structured API requests for sports event creation
+## Usage
+### Installation
+```bash
+pip install transformers peft torch
+```
+### Quick Start
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+import json
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    "google/gemma-2-2b-it",
+    device_map="auto",
+    dtype=torch.float16
+)
+# Load fine-tuned adapter
+model = PeftModel.from_pretrained(base_model, "YOUR_USERNAME/gemma-event-parser")
+tokenizer = AutoTokenizer.from_pretrained("YOUR_USERNAME/gemma-event-parser")
+# Define function schema
+function_schema = {
+    "name": "create_sports_event",
+    "description": "Create a new sports event from natural language description",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "sport": {"type": "string", "description": "Sport type (e.g., Soccer, Basketball, Tennis)"},
+            "venue_name": {"type": "string", "description": "Venue name"},
+            "start_time": {"type": "string", "description": "ISO 8601 format (e.g., 2026-02-07T16:00:00Z)"},
+            "max_participants": {"type": "integer", "default": 2},
+            "event_type": {
+                "type": "string",
+                "enum": ["Casual", "Light Training", "Looking to Improve", "Competitive Game"],
+                "default": "Casual"
+            }
+        },
+        "required": ["sport", "venue_name", "start_time"]
+    }
+}
+# Parse natural language
+def parse_event(user_query):
+    prompt = f"""<start_of_turn>user
+{user_query}
+Available functions:
+{json.dumps([function_schema], indent=2)}<end_of_turn>
+<start_of_turn>model
+"""
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=256,
+        temperature=0.1,
+        do_sample=True,
+        top_p=0.95
+    )
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    # Extract JSON
+    start = result.find("<function_call>") + len("<function_call>")
+    end = result.find("</function_call>")
+    function_call = json.loads(result[start:end].strip())
+    return function_call["arguments"]
+# Example
+query = "I want to play soccer this week Friday 4 PM @ Central Park"
+event_json = parse_event(query)
+print(json.dumps(event_json, indent=2))
+```
+**Output:**
+```json
+{
+  "sport": "Soccer",
+  "venue_name": "Central Park",
+  "start_time": "2026-02-07T16:00:00Z",
+  "max_participants": 22,
+  "event_type": "Casual"
+}
+```
+## Examples
+| Input | Output |
+|-------|--------|
+| "Basketball game tomorrow 6pm at Riverside Courts, competitive" | `{"sport": "Basketball", "venue_name": "Riverside Courts", "start_time": "2026-02-07T18:00:00Z", "max_participants": 10, "event_type": "Competitive Game"}` |
+| "Tennis match Wednesday 10 AM Ashburn Park, looking to improve" | `{"sport": "Tennis", "venue_name": "Ashburn Park", "start_time": "2026-02-12T10:00:00Z", "max_participants": 2, "event_type": "Looking to Improve"}` |
+| "Casual volleyball Saturday 2pm Beach Courts" | `{"sport": "Volleyball", "venue_name": "Beach Courts", "start_time": "2026-02-08T14:00:00Z", "max_participants": 12, "event_type": "Casual"}` |
 ## Training Details
 ### Training Data
+Fine-tuned on synthetic examples covering:
+- Multiple sports (Soccer, Basketball, Tennis, Volleyball, Badminton, etc.)
+- Various time formats (relative dates, specific times)
+- All event types (Casual, Light Training, Looking to Improve, Competitive Game)
+- Different venue patterns
+**Training Size:** ~10-20 high-quality examples (LoRA requires less data)
+### Training Hyperparameters
+- **LoRA Rank (r):** 16
+- **LoRA Alpha:** 32
+- **Target Modules:** `q_proj, k_proj, v_proj, o_proj`
+- **Learning Rate:** 2e-4
+- **Epochs:** 20
+- **Batch Size:** 2 (with gradient accumulation: 4)
+- **Optimizer:** AdamW
+- **Scheduler:** Cosine with warmup
+- **Precision:** FP16
+- **Training Time:** ~1-2 minutes on free Colab
+### Framework Versions
+- **Transformers:** 4.x
+- **PEFT:** 0.18.1
+- **PyTorch:** 2.x
+- **Python:** 3.10+
+## Limitations
+- **Date Parsing:** Currently handles relative dates ("Friday", "tomorrow") but assumes current week context
+- **Time Zones:** Defaults to UTC (Z suffix)
+- **Sports Coverage:** Best performance on common sports; may need examples for niche sports
+- **Language:** English only
+## Intended Use
+✅ **Good for:**
+- Converting casual user input to structured API requests
+- Sports event management applications
+- Voice-to-API integrations
+- Chatbot backends for sports booking
+❌ **Not suitable for:**
+- Mission-critical systems without validation
+- Non-English languages
+- Complex multi-event scheduling
+- Historical date parsing
+## License
+This adapter follows the [Gemma License](https://ai.google.dev/gemma/terms). The base model is subject to Google's Gemma terms of use.
+## Citation
+If you use this model, please cite:
+```bibtex
+@misc{gemma-event-parser-2026,
+  author = {YOUR_NAME},
+  title = {Gemma 2B Event Parser - Sports Event Function Calling},
+  year = {2026},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/YOUR_USERNAME/gemma-event-parser}
+}
+```
+## Acknowledgments
+- Base model: Google's Gemma 2B-IT
+- Fine-tuning framework: Hugging Face PEFT
+- Training compute: Google Colab
+---
+**Questions?** Open an issue or discussion on this model's page!