Update README

README.md

@@ -3,200 +3,91 @@ base_model: unsloth/gemma-7b-bnb-4bit
 library_name: peft
 ---
 
-# Model Card for Model ID
+# Gemma2 Fine-Tuned LoRA Model
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Overview
+This is a **LoRA (Low-Rank Adaptation)** fine-tuned model based on the **`unsloth/gemma-7b-bnb-4bit`** base model. It has been adapted for a **tipification analysis task** similar to the Llama-3.2-3B-Instruct LoRA fine-tuning, where the model classifies text into categories such as **"ESTAFA," "ROBO," "HURTO,"** and their **"TENTATIVA DE"** variations.
 
+During fine-tuning, only specific adapter layers were trained (\~50 million parameters), while the rest of the base model was frozen. This approach allows parameter-efficient training, significantly reducing computational costs.
 
+---
 
-## 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
+## Key Features
+- **Base Model**: `unsloth/gemma-7b-bnb-4bit`
+- **Task Type**: Causal Language Modeling (`CAUSAL_LM`)
+- **LoRA Parameters**:
+  - `r`: 16
+  - `lora_alpha`: 16
+  - `lora_dropout`: 0.0
+- **Target Modules**:
+  - `gate_proj`, `up_proj`, `down_proj`, `k_proj`, `q_proj`, `o_proj`, `v_proj`
+- **Number of Trainable Parameters**: **50,003,968**
+- **Training Loss & Validation Loss**:
+  - Observed over **117 steps** (1 epoch).
+  - See table below for detailed step-by-step values.
 
-<!-- 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.
+## Dataset Distribution
+This model was fine-tuned on the **same dataset** as the Llama-3.2-3B-Instruct LoRA version, with the following category distribution:
 
-## How to Get Started with the Model
+| **Category**             | **Count** | **Percentage** |
+|--------------------------|-----------|----------------|
+| ESTAFA                   | 4610      | 47.3%          |
+| ROBO                     | 2307      | 23.7%          |
+| HURTO                    | 2141      | 22.0%          |
+| TENTATIVA DE ESTAFA      | 306       | 3.1%           |
+| TENTATIVA DE ROBO        | 272       | 2.8%           |
+| TENTATIVA DE HURTO       | 113       | 1.2%           |
+| **Total**                | 9749      | 100%           |
 
-Use the code below to get started with the model.
+Although the dataset has nearly 10K examples in this summary table, the fine-tuning run used an extended version (\~15K examples) for this particular training session.
 
-[More Information Needed]
+---
 
 ## Training Details
+- **Hardware**: Single GPU A100 40Gb
+- **Num Examples**: ~15,000
+- **Epochs**: 1
+- **Batch Size per Device**: 32
+- **Gradient Accumulation Steps**: 4
+- **Effective Total Batch Size**: 128
+- **Total Steps**: 117
+- **Number of Trainable Parameters**: 50,003,968
+
+### Training and Validation Loss
+Below is a snapshot of how training and validation loss evolved during the single epoch (117 steps):
+
+| **Step** | **Training Loss** | **Validation Loss** |
+|----------|-------------------|---------------------|
+| 10       | 2.974900          | 4.242294            |
+| 20       | 5.451000          | 4.526450            |
+| 30       | 4.150400          | 3.632928            |
+| 40       | 3.036100          | 2.615031            |
+| 50       | 2.492900          | 2.178700            |
+| 60       | 2.095400          | 1.886430            |
+| 70       | 2.099200          | 1.548187            |
+| 80       | 1.983100          | 2.104600            |
+| 90       | 2.020900          | 1.526225            |
+| 100      | 1.727700          | 1.699223            |
+| 110      | 1.868300          | 1.716561            |
+| ...      | ...               | ...                 |
+
+Final training concluded at **step 117**.  
+We observe a steady decrease in both training and validation losses, indicating the model was converging throughout the single epoch.
 
-### 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]
+## Deployment Instructions
+You can use this LoRA fine-tuned model with the Hugging Face Transformers library. Below is an example of how to load and run the model for text generation or classification-like tasks:
 
-## Model Card Contact
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-[More Information Needed]
-### Framework versions
+tokenizer = AutoTokenizer.from_pretrained("Petermoyano/unsloth-gemma-7b-bnb-4bit-LoRA-Tipification-CausalLM-16R-16Alpha-1Epoch")
+model = AutoModelForCausalLM.from_pretrained("Petermoyano/unsloth-gemma-7b-bnb-4bit-LoRA-Tipification-CausalLM-16R-16Alpha-1Epoch")
 
-- PEFT 0.14.0
+input_text = "TENTATIVA DE ESTAFA:"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=50)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))