Update README.md

README.md

@@ -1,45 +1,93 @@
+---
+language:
+- en
+license: apache-2.0
+library_name: unsloth
+tags:
+- llama
+- llama-3
+- text-generation
+- deep-learning
+- image-analysis
+- deepfake-detection
+- lora
+- fine-tuning
+datasets:
+- custom
+pipeline_tag: text-generation
+---
 
-    # Deepfake Explanation Model based on Llama 3.2
-
-    This model is fine-tuned to provide technical and non-technical explanations of deepfake detection results.
-    It analyzes detection metrics, activation regions, and image features to explain why an image was classified as real or fake.
-
-    ## Model Details
-    - Base model: Llama 3.2 3B Instruct
-    - Training method: LoRA fine-tuning with Unsloth
-    - Training data: Custom dataset of deepfake detection results with expert explanations
-    
-    ## Usage Example
-    ```python
-    from unsloth import FastLanguageModel
-    
-    model, tokenizer = FastLanguageModel.from_pretrained(
-        model_name="{repo_name}",
-        max_seq_length=2048,
-        load_in_4bit=True,
-    )
-    FastLanguageModel.for_inference(model)
-    
-    # Format input
-    messages = [
-        {"role": "user", "content": "Analyze this deepfake detection result..."}
-    ]
-    
-    # Generate explanation
-    inputs = tokenizer.apply_chat_template(
-        messages,
-        tokenize=True,
-        add_generation_prompt=True,
-        return_tensors="pt",
-    ).to("cuda")
-    
-    from transformers import TextStreamer
-    text_streamer = TextStreamer(tokenizer, skip_prompt=True)
-    _ = model.generate(
-        input_ids=inputs,
-        streamer=text_streamer,
-        max_new_tokens=800,
-        temperature=0.7
-    )
-    ```
-    
+# Deepfake Explanation Model based on Llama 3.2
+
+This model is fine-tuned to provide technical and non-technical explanations of deepfake detection results. It analyzes detection metrics, activation regions, and image features to explain why an image was classified as real or fake.
+
+## Model Details
+- Base model: Llama 3.2 3B Instruct
+- Training method: LoRA fine-tuning with Unsloth
+- Training data: Custom dataset of deepfake detection results with expert explanations
+
+## Use Cases
+
+This model can be used to:
+- Generate expert-level technical explanations of deepfake detection results
+- Provide simplified, accessible explanations for non-technical audiences
+- Analyze activation regions in images to explain detection decisions
+- Support educational content about deepfake detection
+
+## Usage Example
+
+```python
+from unsloth import FastLanguageModel
+import torch
+
+# Load the model
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="saakshigupta/deepfake-explainer-llama32",
+    max_seq_length=2048,
+    load_in_4bit=True,
+)
+
+# Enable for inference
+FastLanguageModel.for_inference(model)
+
+# Example prompt
+prompt = """Analyze this deepfake detection result and provide both a technical expert explanation and a simple non-technical explanation.
+
+Below is a deepfake detection result with explanation metrics. Provide both a technical and accessible explanation of why this image is classified as it is.
+### Detection Results:
+Verdict: Deepfake
+Confidence: 0.87
+### Analysis Metrics:
+High Activation Regions: lips, nose
+Medium Activation Regions: eyes, chin
+Low Activation Regions: forehead, background
+Frequency Analysis Score: 0.79
+### Image Description:
+A man with glasses and short hair looking directly at the camera.
+### Heatmap Description:
+The heatmap shows intense red coloration around the lips and nose area, suggesting these regions contributed most to the detection verdict."""
+
+# Format for chat
+messages = [
+    {"role": "user", "content": prompt},
+]
+
+# Apply chat template 
+inputs = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt",
+).to("cuda" if torch.cuda.is_available() else "cpu")
+
+# Generate response
+from transformers import TextStreamer
+text_streamer = TextStreamer(tokenizer, skip_prompt=True)
+_ = model.generate(
+    input_ids=inputs, 
+    streamer=text_streamer,
+    max_new_tokens=800,
+    use_cache=True,
+    temperature=0.7,
+    do_sample=True
+)