Update README.md

README.md

@@ -1,58 +1,70 @@
 ---
+license: gemma
 library_name: transformers
-license: other
-base_model: google/functiongemma-270m-it
 tags:
-- llama-factory
-- full
-- generated_from_trainer
-model-index:
-- name: FunctionGemma_Director_V1
-  results: []
+- gemma
+- video-production
+- automation
+- viral-content
+- function-calling
+base_model: google/functiongemma-270m-it
+pipeline_tag: text-generation
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
-# FunctionGemma_Director_V1
-
-This model is a fine-tuned version of [google/functiongemma-270m-it](https://huggingface.co/google/functiongemma-270m-it) on the game_director dataset.
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 5e-05
-- train_batch_size: 2
-- eval_batch_size: 8
-- seed: 42
-- gradient_accumulation_steps: 8
-- total_train_batch_size: 16
-- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_ratio: 0.1
-- num_epochs: 3.0
-
-### Training results
-
-
-
-### Framework versions
-
-- Transformers 4.57.1
-- Pytorch 2.9.0+cu126
-- Datasets 4.0.0
-- Tokenizers 0.22.1
+# 🎬 FunctionGemma-Director-V1
+
+**FunctionGemma-Director-V1** is a specialized lightweight AI model (270M parameters) designed to automate the production of viral short-form gaming videos (TikTok/Shorts/Reels).
+
+It acts as a **"Creative Director"**, converting a simple video title into a structured **JSON editing plan**, executing a "Trojan Horse" monetization strategy by seamlessly integrating CPA offers into content.
+
+## 🚀 Key Features
+*   **Size:** ~540MB (Runs smoothly on free Colab/CPU).
+*   **Strategy:** Automatically places "High Retention Hooks" and injects "CPA Offers" at the most effective timestamps.
+*   **Output:** Strict JSON format compatible with Python video automation engines (MoviePy).
+
+## 💻 How to Use
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import json
+
+# 1. Load the Model
+model_id = "Saad4web/FunctionGemma-Director-V1"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, 
+    device_map="auto", 
+    torch_dtype=torch.float16 # Optimized for low memory
+)
+
+# 2. Define the Tools (The Model's Vocabulary)
+tools_schema = [
+    {"name": "add_video_clip", "parameters": {"file_path": "string", "duration": "number"}},
+    {"name": "add_text_overlay", "parameters": {"text": "string", "color": "string"}},
+]
+
+# 3. Create the Prompt
+video_title = "TOP 3|SCARIEST HORROR GAMES|*DONT WATCH ALONE*"
+system_msg = f"You are a specialized video editor AI. Available tools: {json.dumps(tools_schema)}"
+messages = [{"role": "user", "content": system_msg + f"\n\nCreate a viral video plan for: {video_title}"}]
+
+# 4. Generate the Plan
+input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    input_ids, 
+    max_new_tokens=512, 
+    do_sample=True, 
+    temperature=0.1
+)
+
+# 5. Get the JSON
+plan = tokenizer.decode(outputs[0][len(input_ids[0]):], skip_special_tokens=True)
+print(plan)
+
+#🛠️ Training Details
+    Architecture: Fine-tuned google/functiongemma-270m-it.
+    Dataset: Synthetic dataset generated via Knowledge Distillation (Teacher: GPT-4o/Gemini 2.0).
+    Method: Full Fine-Tuning using LLaMA Factory.
+    Created by [Saad4web]