Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+- he
+base_model:
+- Raziel1234/Duchifat-2
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- chemistry
+- agent
+- medical
+- climate
+- code
+- art
+- music
+- legal
+- finance
+- biology
+- text-generation-inference
+- Pytorch
+- causal_lm
+---
+
+# 🚀 Duchifat-V2-Instruct (דוכיפת 2) | Official Model Card
+
+## 📝 Executive Summary
+**Duchifat-V2-Instruct** is a fine-tuned, instruction-following version of the Duchifat-V2 architecture (136M parameters). Developed by **TopAI**, this model is specifically optimized for creative content generation, bilingual dialogue, and task-oriented text processing. 
+
+While the base model provides a massive knowledge foundation from 3.27 billion tokens, the **Instruct** version has undergone targeted fine-tuning to transform it from a "text completer" into a **creative writer** capable of following complex prompts with a unique, human-like voice.
+
+---
+
+## 🏗️ Technical Specifications
+| Component | Specification | Description |
+| :--- | :--- | :--- |
+| **Parameters** | 136 Million | Optimized for edge deployment and real-time inference. |
+| **Architecture** | Decoder-only Transformer | Enhanced for causal reasoning and fluency. |
+| **Layers / Heads** | 12 / 12 | Deep representation for nuanced semantics. |
+| **Context Window** | 1024 Tokens | Supports creative long-form generation. |
+| **Tokenizer** | DictaLM 2.0 | High-efficiency sub-word tokenization for Hebrew/English. |
+| **Training Phase** | Post-5 Epoch Instruct | Refined for instruction-following & EOS consistency. |
+
+---
+
+## 🎨 Model Capabilities & "The Creative Writer"
+Unlike standard small-scale models, **Duchifat-V2-Instruct** exhibits "Creative Personality." It excels at:
+* **Narrative Writing:** Crafting stories and monologues with emotional depth.
+* **Instruction Following:** Responding to specific system prompts and user constraints.
+* **Bilingual Versatility:** Seamlessly switching between Hebrew and English based on the prompt's linguistic context.
+* **Marketing & Copywriting:** Generating slogans, blog posts, and creative ads.
+
+> **Note:** Due to its training on the C4 corpus, the model retains a vast "general knowledge" base, allowing it to act as a sophisticated creative partner rather than a purely technical agent.
+
+---
+
+## 📊 Training Infrastructure
+* **Dataset:** Curated **C4** (3.27B Tokens) - 50% Hebrew, 50% English.
+* **Fine-Tuning:** Instruction-tuning on high-quality conversational and creative datasets.
+* **Optimization:** AdamW with a focus on preserving the pre-trained knowledge (Knowledge Retention).
+
+---
+
+## 💻 Implementation & Inference
+
+To utilize the Instruct capabilities, use the following structure:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+MODEL_ID = "TopAI-1/Duchifat-2-Instruct"
+
+def run_duchifat_chat():
+    print("--- Loading Duchifat-2 (Post 5-Epoch Instruct Training) ---")
+    
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_ID, 
+        trust_remote_code=True, 
+        torch_dtype=torch.bfloat16,
+        device_map="auto"
+    )
+    model.eval()
+    model.config.use_cache = False 
+
+    chat_history = []
+    
+    print("--- Model Ready! ---")
+    
+    while True:
+        user_input = input("\nהכנס הוראה (או 'יציאה'): ")
+        if user_input.lower() in ["exit", "quit", "יציאה"]:
+            break
+            
+        # Add current instruction to memory
+        chat_history.append(f"Instruction: {user_input}")
+        
+        # Build prompt with history
+        full_prompt = "\n".join(chat_history) + "\nContent:"
+        
+        inputs = tokenizer(full_prompt, return_tensors="pt").to(model.device)
+        
+        # Context Window Protection (Max 1024 tokens)
+        if inputs.input_ids.shape[1] > 850:
+            chat_history = chat_history[2:] # Trim oldest turn
+            full_prompt = "\n".join(chat_history) + "\nContent:"
+            inputs = tokenizer(full_prompt, return_tensors="pt").to(model.device)
+
+        with torch.no_grad():
+            output_tokens = model.generate(
+                input_ids=inputs.input_ids,
+                attention_mask=inputs.attention_mask,
+                max_new_tokens=300, # Increased for creative writing
+                do_sample=True,
+                temperature=0.75, 
+                top_p=0.9,
+                repetition_penalty=1.15,
+                pad_token_id=tokenizer.eos_token_id,
+                use_cache=False
+            )
+        
+        full_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
+        
+        # Extract only the latest response
+        parts = full_text.split("Content:")
+        answer = parts[-1].strip()
+        
+        # Save response to history for context
+        chat_history.append(f"Content: {answer}")
+        
+        print(f"\nדוכיפת-2: {answer}")
+
+if __name__ == "__main__":
+    run_duchifat_chat()