Update README.md with detailed model card for Trump Mistral Adapter

README.md

@@ -1,202 +1,154 @@
 ---
+language:
+- en
+tags:
+- mistral
+- lora
+- adapter
+- fine-tuned
+- politics
+- conversational
+license: mit
+datasets:
+- pookie3000/trump-interviews
+- bananabot/TrumpSpeeches
 base_model: mistralai/Mistral-7B-Instruct-v0.2
 library_name: peft
 ---
 
-# Model Card for Model ID
+# 🇺🇸 Trump Mistral Adapter 🇺🇸
+
+> *"This adapter, believe me folks, it's tremendous. It's the best adapter, everyone says so."* 
+
+This LoRA adapter for [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) has been fine-tuned to emulate Donald Trump's distinctive speaking style, discourse patterns, and policy positions. The model combines the unique rhetorical flourishes, speech cadence, and characteristic expressions associated with the former U.S. President.
+
+## ✨ Model Details
+
+| Feature | Description |
+|---------|-------------|
+| **Base Model** | [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) |
+| **Architecture** | LoRA adapter (Low-Rank Adaptation) |
+| **LoRA Rank** | 16 |
+| **Language** | English |
+| **Training Focus** | Trump's communication style, rhetoric, and response patterns |
+| **Merged Adapters** | Combines style and identity LoRA weights from:<br>- nnat03/trump-mistral-adapter (original adapter)<br>- ./identity-adapters/trump-identity-adapter |
+
+## 🎯 Intended Use
+
+<div align="center">
+  <table>
+    <tr>
+      <td align="center">📚 <b>Education</b></td>
+      <td align="center">🔍 <b>Research</b></td>
+      <td align="center">🎭 <b>Creative</b></td>
+    </tr>
+    <tr>
+      <td>Political discourse analysis</td>
+      <td>Rhetoric pattern studies</td>
+      <td>Interactive simulations</td>
+    </tr>
+  </table>
+</div>
+
+## 📊 Training Data
+
+This model was trained on carefully curated datasets that capture authentic speech patterns:
+
+- 🎤 [Trump interviews dataset](https://huggingface.co/datasets/pookie3000/trump-interviews) - Extensive collection of interview transcripts
+- 📢 [Trump speeches dataset](https://huggingface.co/datasets/bananabot/TrumpSpeeches) - Comprehensive archive of public speeches
+
+These datasets were processed into a specialized instruction format to optimize learning of distinctive speech patterns.
+
+## ⚙️ Technical Specifications
+
+### Training Configuration
+
+```
+🧠 Framework: Hugging Face Transformers + PEFT
+📊 Optimization: 4-bit quantization
+🔧 LoRA Config: r=16, alpha=64, dropout=0.05
+🎛️ Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+```
+
+### Training Parameters
+
+```
+📦 Batch size: 4
+🔄 Gradient accumulation: 4
+📈 Learning rate: 2e-4
+🔁 Epochs: 3
+📉 LR scheduler: cosine
+⚡ Optimizer: paged_adamw_8bit
+🧮 Precision: BF16
+```
+
+## ⚠️ Limitations and Biases
+
+- This model mimics a speaking style but doesn't guarantee factual accuracy
+- While emulating Trump's rhetoric, it doesn't represent his actual views
+- May reproduce biases present in the training data
+- Not suitable for production applications without additional fact-checking
+
+## 💻 Usage
+
+Run this code to start using the adapter with the Mistral-7B-Instruct-v0.2 base model:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from peft import PeftModel
+import torch
+
+# Load base model with 4-bit quantization
+base_model_id = "mistralai/Mistral-7B-Instruct-v0.2"
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.float16,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_use_double_quant=True,
+)
+
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    quantization_config=bnb_config,
+    device_map="auto",
+    torch_dtype=torch.float16
+)
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+
+# Apply the adapter
+model = PeftModel.from_pretrained(model, "nnat03/trump-mistral-adapter")
+
+# Generate a response
+prompt = "What's your plan for border security?"
+input_text = f"<s>[INST] {prompt} [/INST]"
+inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_length=512, temperature=0.7, do_sample=True)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response.split("[/INST]")[-1].strip())
+```
+
+## 📚 Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{nnat03-trump-mistral-adapter,
+  author = {nnat03},
+  title = {Trump Mistral Adapter},
+  year = {2023},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/nnat03/trump-mistral-adapter}}
+}
+```
+
+## 🔍 Ethical Considerations
+
+This model is created for educational and research purposes. It attempts to mimic the speaking style of a public figure but does not represent their actual views or statements. Use responsibly.
 
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-## Model Details
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-## Uses
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-### Direct Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-## Evaluation
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-#### Summary
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-## Environmental Impact
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-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).
-
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-## Technical Specifications [optional]
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-### Framework versions
+---
 
-- PEFT 0.15.0
+<div align="center">
+  <p><b>Framework version:</b> PEFT 0.15.0</p>
+  <p>Made with ❤️ for NLP research and education</p>
+</div>