| --- |
| library_name: transformers |
| tags: |
| - duchifat |
| - agent |
| - chemistry |
| - biology |
| - art |
| - medical |
| - climate |
| - text-generation-inference |
| - finance |
| - music |
| - legal |
| - PyTorch |
| - fine-tuned |
| - instruct |
| license: apache-2.0 |
| language: |
| - he |
| - en |
| base_model: |
| - Raziel1234/Duchifat-2 |
| pipeline_tag: text-generation |
| --- |
| |
| # Duchifat-2.1-Instruct: Technical Model Card & Documentation |
|
|
| ## 1. Executive Summary |
| Duchifat-2.1-Instruct is a specialized Small Language Model (SLM) developed by razielAI at TopAI. The project aims to bridge the gap between compact model efficiency and high-density reasoning in bilingual (Hebrew/English) environments. |
|
|
| This model is a Full Parameter Fine-Tuned (FPFT) version of the Duchifat-2 architecture, specifically designed to serve as a baseline for instruction-following tasks, technical scripting, and brand-aligned communication. |
|
|
| --- |
|
|
| ## 2. Model Architecture & Training Philosophy |
| * **Core Architecture:** Optimized Transformer Decoder-only. |
| * **Parameter Count:** ~136M (Ultra-compact). |
| * **Fine-Tuning Method:** Supervised Fine-Tuning (SFT) focusing on Identity Injection and Logic Consistency. |
| * **Objective:** To provide a low-latency "Reasoning Engine" that can run on consumer-grade hardware without compromising on technical accuracy in English. |
|
|
| --- |
|
|
| ## 3. Targeted Competencies |
|
|
| ### A. Technical Task Execution (English) |
| The model is optimized for software engineering workflows, including: |
| * **Modern Web Dev:** Scaffolding React applications with Vite and TypeScript. |
| * **Python Automation:** System monitoring, data processing, and asynchronous programming. |
| * **Logic Flow:** Structured step-by-step problem solving for algorithmic queries. |
|
|
| ### B. Hebrew Identity & Alignment |
| Duchifat-2.1-Instruct is trained to represent the TopAI professional persona. It maintains a consistent "Senior Consultant" tone in Hebrew, making it suitable for internal automation and customer-facing interfaces. |
|
|
| ### C. RAG (Retrieval-Augmented Generation) Compatibility |
| The model's training emphasized "Faithfulness to Prompt," which is a critical requirement for RAG systems. It is designed to act as a synthesizer of external knowledge bases. |
|
|
| --- |
|
|
| ## 4. Implementation Guide |
|
|
| ### Installation |
| ```bash |
| pip install transformers torch accelerate |
| ``` |
|
|
| ### Usage Pattern |
| ```python |
| import torch |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| # 1. הגדרות המודל מה-Hub |
| model_id = "razielAI/Duchifat-2.1-Instruct" |
| device = "cuda" if torch.cuda.is_available() else "cpu" |
| |
| print(f"🔄 מתחיל טעינה של {model_id} מה-Hugging Face Hub...") |
| |
| # 2. טעינת טוקנייזר ומודל (עם trust_remote_code כי זה מודל מותאם) |
| 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" if device == "cuda" else None |
| ).to(device) |
| |
| model.eval() |
| |
| def run_duchifat_inference(user_prompt): |
| # הפורמט המדויק שהמודל מכיר מהאימון |
| full_prompt = f"<instruction> {user_prompt} </instruction>\n<assistant> " |
| |
| # הכנת ה-Inputs |
| inputs = tokenizer(full_prompt, return_tensors="pt", add_special_tokens=False).to(device) |
| |
| with torch.no_grad(): |
| outputs = model.generate( |
| input_ids=inputs["input_ids"], |
| attention_mask=inputs["attention_mask"], |
| max_new_tokens=20, # הגדלתי מעט לטובת תשובות קוד מפורטות |
| do_sample=False, # דטרמיניסטי לטובת דיוק טכני |
| repetition_penalty=4.5, # מניעת חזרתיות במודל קטן |
| eos_token_id=tokenizer.eos_token_id, |
| pad_token_id=tokenizer.eos_token_id |
| ) |
| |
| # פיענוח הטקסט המלא |
| full_text = tokenizer.decode(outputs[0], skip_special_tokens=False) |
| |
| # לוגיקת חילוץ נקייה של התשובה מתוך ה-Tags |
| if "<assistant>" in full_text: |
| # לוקחים מה שבא אחרי assistant וחותכים ב-eos או בסגירת תגית |
| response = full_text.split("<assistant>")[-1] |
| response = response.replace("</assistant>", "").replace("<eos>", "").strip() |
| else: |
| response = full_text.strip() |
| |
| return response |
| |
| # --- לולאת צ'אט אינטראקטיבית --- |
| print("\n" + "="*60) |
| print("🚀 Duchifat-2.1-Instruct: Cloud Mode (Loaded from HF Hub)") |
| print("Identity: TopAI | Language: Hebrew & English | Ready for instructions.") |
| print("הקלד 'exit' או 'יציאה' כדי לסיים.") |
| print("="*60) |
| |
| while True: |
| try: |
| user_input = input("\n👤 You: ") |
| |
| if user_input.lower() in ["exit", "quit", "יציאה"]: |
| print("\n🤖 Duchifat-2.1: Closing session. Standby for the next mission... 👋") |
| break |
| |
| if not user_input.strip(): |
| continue |
| |
| # הרצת האינפרנס |
| response = run_duchifat_inference(user_input) |
| |
| # הדפסת התשובה |
| print(f"\n🤖 Duchifat-2.1: {response}") |
| |
| except KeyboardInterrupt: |
| break |
| except Exception as e: |
| print(f"\n❌ Runtime Error: {e}") |
| |
| print("\n" + "="*60) |
| ``` |
|
|
| --- |
|
|
| ## 5. Performance Evaluation (TBD) |
| *Note: Formal benchmarking and metric evaluation (e.g., MMLU, HumanEval) for this specific fine-tuned version are currently in progress.* |
|
|
| ### Key Evaluation Areas: |
| * **Code Reliability:** Accuracy of generated syntax in Python/JS. |
| * **Instruction Adherence:** Success rate in following complex multi-step prompts. |
| * **Brand Consistency:** Alignment with the TopAI persona over long-turn conversations. |
| * **Latency:** Tokens-per-second measurement across various hardware (CPU/GPU). |
|
|
| --- |
|
|
| ## 6. Deployment & Quantization |
| Duchifat-2.1-Instruct's compact size makes it a prime candidate for: |
| * **Edge Computing:** Deployment on mobile devices or IoT gateways. |
| * **Private Cloud:** Secure, on-premise inference with minimal VRAM requirements. |
| * **Scalability:** High-throughput processing for microservices. |
|
|
| --- |
|
|
| ## 7. Ethical Considerations & Constraints |
| * **SLM Scope:** Users should note that as an SLM, the model excels at specific instructions rather than open-ended creative writing. |
| * **Bilingual Nuance:** While highly capable, users are encouraged to validate complex Hebrew grammar for high-stakes formal documentation. |
| * **Safety:** Standard LLM guardrails apply; the model should be used in conjunction with input/output filtering for production environments. |
|
|
| --- |
|
|
| ## 8. About TopAI |
| TopAI is an AI research and development hub focused on practical, efficient, and aligned AI solutions. |
|
|
| **Lead Developer:** Raziel |
| **Organization:** TopAI |
| **Status:** Version 2.1.0-Instruct (Active Development) |
