Update README.md

README.md

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 ---
-library_name: transformers
-tags: []
+license: apache-2.0
+base_model: mistralai/Mistral-7B-v0.1
+tags:
+- pokemon
+- text-generation
+- fine-tuned
+- mistral
+- creative-writing
+language:
+- en
+pipeline_tag: text-generation
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# 🐾 Pokemon Generator - Mistral 7B
 
+A fine-tuned Mistral 7B model trained to generate original Pokemon with authentic names, types, and descriptions.
 
 ## 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**: Mistral 7B v0.1
+- **Fine-tuning Method**: LoRA (Low-Rank Adaptation) 
+- **Training Data**: Pokemon from Generations 1-4
+- **Model Size**: ~13GB
+- **Languages**: English
+- **License**: Apache 2.0
+
+## Quick Start
+
+### Basic Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("Sulfruos/poke-generator-demo")
+tokenizer = AutoTokenizer.from_pretrained("Sulfruos/poke-generator-demo")
+
+# Generate a Pokemon
+prompt = "Generate a new and original Pokemon that doesn't exist yet: "
+inputs = tokenizer(prompt, return_tensors="pt")
+
+# Generate with custom parameters
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=100,
+    temperature=0.7,
+    do_sample=True,
+    top_p=0.95,
+    top_k=40,
+    repetition_penalty=1.2,
+    pad_token_id=tokenizer.eos_token_id
+)
+
+# Decode result
+result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(result)
+```
+
+### Using Pipeline
+
+```python
+from transformers import pipeline
+
+# Create text generation pipeline
+generator = pipeline(
+    "text-generation", 
+    model="Sulfruos/poke-generator-demo",
+    tokenizer="Sulfruos/poke-generator-demo"
+)
+
+# Generate Pokemon
+result = generator(
+    "Generate a new and original Pokemon that doesn't exist yet: ",
+    max_new_tokens=100,
+    temperature=0.7,
+    do_sample=True
+)
+
+print(result[0]['generated_text'])
+```
+
+## Example Outputs
+
+### Fire-type Pokemon
+```
+Name: Blazefin
+Type: Fire/Water
+Description: This unique Pokemon can survive in both lava and deep ocean trenches. Its fins glow red-hot when threatened, boiling the water around it as a defense mechanism.
+```
+
+### Psychic-type Pokemon  
+```
+Name: Mindwhisper
+Type: Psychic
+Description: Known for its ability to communicate telepathically across vast distances. Its large, luminous eyes can see into the dreams of sleeping Pokemon.
+```
+
+### Dual-type Pokemon
+```
+Name: Crystaleon
+Type: Ice/Electric
+Description: Its crystalline body stores electrical energy from thunderstorms. When it moves, tiny ice crystals spark with electricity, creating beautiful aurora-like displays.
+```
+
+## Recommended Parameters
+
+### Conservative Generation
+```python
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=80,
+    temperature=0.5,      # More consistent
+    top_p=0.9,
+    repetition_penalty=1.1
+)
+```
+
+### Creative Generation
+```python
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=120,
+    temperature=0.9,      # More creative
+    top_p=0.95,
+    top_k=50,
+    repetition_penalty=1.3
+)
+```
+
+### Balanced Generation (Recommended)
+```python
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=100,
+    temperature=0.7,      # Good balance
+    top_p=0.95,
+    top_k=40,
+    repetition_penalty=1.2
+)
+```
+
+## Output Format
+
+The model generates Pokemon in this consistent format:
+
+```
+Name: [Pokemon Name]
+Type: [Type] or [Type1/Type2]
+Description: [Detailed description of the Pokemon's appearance, abilities, and characteristics]
+```
 
 ## 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]
-
+### Dataset
+- Source: Pokemon data from Generations 1-4
+- Size: ~400 authentic Pokemon entries
+- Format: Structured Name/Type/Description format
+- Preprocessing: Name uniqueness validation, type consistency checks
 
-#### Training Hyperparameters
+### Training Process
+1. **Base Model**: Mistral 7B v0.1
+2. **Method**: LoRA fine-tuning (r=16, α=32)
+3. **Hardware**: Google Colab T4 GPU
+4. **Training Time**: ~45 minutes
+5. **Loss Reduction**: 2.13 → 0.95
+6. **Validation**: Format consistency and uniqueness checks
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+### Model Architecture
+- **Parameters**: ~7B (base) + LoRA adapters
+- **Context Length**: 4096 tokens
+- **Vocabulary**: 32,000 tokens
+- **Precision**: fp16 (GPU) / fp32 (CPU)
 
-#### Speeds, Sizes, Times [optional]
+## Hardware Requirements
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+### Minimum Requirements
+- **RAM**: 16GB+ 
+- **Storage**: 15GB free space
+- **GPU**: Optional but recommended (GTX 1060+ or equivalent)
 
-[More Information Needed]
+### Recommended Setup
+- **RAM**: 32GB+
+- **GPU**: RTX 3070+ or T4+ for fast inference
+- **Storage**: SSD recommended for faster loading
 
-## Evaluation
+### Performance Expectations
+- **CPU only**: 30-120 seconds per generation
+- **GPU (GTX 1060)**: 15-45 seconds per generation  
+- **GPU (RTX 3070+)**: 5-15 seconds per generation
+- **GPU (T4/V100)**: 3-10 seconds per generation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+## Use Cases
 
-### Testing Data, Factors & Metrics
+- **Creative Writing**: Generate Pokemon for stories and fan fiction
+- **Game Development**: Create original creatures for Pokemon-inspired games
+- **Educational**: Learn about fine-tuning language models
+- **Research**: Study text generation and creative AI applications
 
-#### Testing Data
+## Limitations
 
-<!-- This should link to a Dataset Card if possible. -->
+- **English only**: Trained on English Pokemon descriptions
+- **Generation 1-4 style**: Reflects classic Pokemon characteristics
+- **Format dependency**: Works best with the specific prompt format
+- **Creativity vs consistency**: Higher temperature = more creative but less consistent
 
-[More Information Needed]
+## Fine-tuning Process
 
-#### Factors
+This model was created using LoRA (Low-Rank Adaptation) fine-tuning:
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+1. **Data Collection**: Gathered authentic Pokemon data
+2. **Preprocessing**: Formatted as Name/Type/Description
+3. **LoRA Training**: Fine-tuned on Google Colab T4
+4. **Validation**: Implemented quality and uniqueness checks
+5. **Merging**: Combined LoRA weights with base model for standalone deployment
 
-[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]
+## Model Card Contact
 
-## More Information [optional]
+- **Created by**: @Sulfruos
+- **Model type**: Text Generation (Pokemon-focused)
+- **Language**: English
+- **License**: Apache 2.0
+- **Base model**: Mistral 7B v0.1
 
-[More Information Needed]
+## Citation
 
-## Model Card Authors [optional]
+If you use this model in your research or projects, please cite:
 
-[More Information Needed]
+```bibtex
+@misc{pokemon-generator-mistral,
+  title={Pokemon Generator: Fine-tuned Mistral 7B for Creative Pokemon Generation},
+  author={Sulfruos},
+  year={2024},
+  howpublished={\\url{https://huggingface.co/Sulfruos/poke-generator-demo}}
+}
+```
 
-## Model Card Contact
+## Ethical Considerations
 
-[More Information Needed]
+This model is designed for creative and educational purposes. Generated content should be used responsibly and in accordance with fair use principles. The model generates fictional creatures inspired by the Pokemon franchise but creates original content.