Duchifat-2

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language:

• he
• en license: apache-2.0 library_name: transformers tags:
• causal-lm
• duchifat
• pytorch-lightning
• hebrew-llm
• bilingual datasets:
• allenai/c4 metrics:
• perplexity

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🚀 Duchifat-V2 (דוכיפת 2) | Official Model Card

📝 Executive Summary

Duchifat-V2 is a state-of-the-art 136M parameter causal language model (LLM) developed by TopAI. Architected from the ground up, Duchifat-V2 is specifically engineered to bridge the gap between Semitic linguistic structures and modern generative AI. By utilizing a custom-tailored tokenizer and a bilingual training strategy, the model achieves high-performance text generation in both Hebrew and English.

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🏗️ Technical Specifications & Architecture

Duchifat-V2 implements a high-efficiency Decoder-only Transformer architecture with the following parameters:

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📊 Training Infrastructure & Dataset

1. Data Composition

The model underwent rigorous training on a curated version of the C4 (Colossal Clean Crawled Corpus), distributed as follows:

• Hebrew (50%): High-quality web data, news, and academic sources.
• English (50%): General knowledge base for cross-lingual transfer learning.

2. Compute & Framework

• Compute: NVIDIA L4 / A100 Tensor Core GPUs.
• Orchestration: PyTorch Lightning (v2.x).
• Optimization: AdamW ($\beta_1=0.9, \beta_2=0.95$).
• Precision: 16-bit Mixed Precision (O2) for memory efficiency.
• Training Steps: ~50,000 Global Steps.
• Effective Batch Size: 64 (via Gradient Accumulation).

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🎯 Key Performance Indicators (KPIs)

• Native Hebrew Fluency: Superior understanding of Hebrew grammar compared to general-purpose multilingual models of similar size.
• Zero-Shot Potential: Demonstrated ability to handle basic logic and continuation tasks without fine-tuning.
• Bilingual Versatility: Seamlessly handles code-switching and bilingual contexts.

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💻 Implementation & Inference

Model Loading

Since Duchifat-V2 uses a custom architecture, ensure the model class (DuchifatCore) is defined in your environment:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# --- הגדרות ---
model_id = "Raziel1234/Duchifat-2"
device = "cuda" if torch.cuda.is_available() else "cpu"

print(" טוען את Duchifat-2: הופך למחולל בלוגים יצירתי...")

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
base_model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    trust_remote_code=True, 
    torch_dtype=torch.bfloat16
).to(device)

if not hasattr(base_model.config, "num_hidden_layers"):
    base_model.config.num_hidden_layers = 12

base_model.eval()

def generate_blog_post(topic_title, language="en"):
    """
    פונקציה ליצירת פוסט לבלוג. 
    היא בונה פתיח שגורם למודל להיכנס למוד של כתיבת תוכן.
    """
    if language == "he":
        prompt = f"פוסט חדש בבלוג: {topic_title}\nהיום אני רוצה לדבר על נושא מרתק שמעסיק אותי רבות. "
    else:
        prompt = f"Blog Post: {topic_title}\nIn today's post, I want to share some unique insights about {topic_title}. This journey began when "

    inputs = tokenizer(prompt, return_tensors="pt").to(device)
    
    with torch.no_grad():
        outputs = base_model.generate(
            **inputs, 
            max_new_tokens=250,      # אורך של פוסט מכובד
            do_sample=True,
            temperature=0.8,         # יצירתיות גבוהה לבלוג
            top_p=0.92,
            repetition_penalty=1.2,
            no_repeat_ngram_size=3,  # מונע חזרתיות מעצבנת
            eos_token_id=tokenizer.eos_token_id,
            pad_token_id=tokenizer.eos_token_id
        )
    
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# --- הרצת בדיקה: יצירת בלוגים ---
print("\n" + "="*40)
print(" כתיבת פוסטים לבלוג (Duchifat-2 Base):")
print("="*40)

# בדיקה 1: בלוג טכנולוגי באנגלית
print(" Blog 1 (English - AI Tech):")
blog_en = generate_blog_post("The Future of AI in Daily Life")
print(blog_en)

print("-" * 30)

# בדיקה 2: בלוג סגנון חיים בעברית
print(" בלוג 2 (עברית - לייף סטייל):")
blog_he = generate_blog_post("החשיבות של זמן איכות עם עצמך", language="he")
print(blog_he)

import requests
import sys
import io

# תיקון עברית לווינדוס
if sys.platform == "win32":
    sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')

URL = "https://rnevo2016--duchifat-2-ultra-fast-streaming-fastapi-app.modal.run/v1/stream"

# מכיוון שזה מודל BASE - תן לו התחלה של משפט (השלמה)
payload = {
    "prompt": "There is something magical about waking up in a city you’ve never visited before. This morning in Rome, the smell of fresh espresso and warm cornetti drifted through the open window of my small Airbnb. As I stepped out onto the cobblestone streets, I realized that the best way to see this city isn't by following a map, but by getting lost in its narrow alleys. My first stop was a tiny bakery where I discovered that",
    "temperature": 0.5,        # נמוך = יותר ממוקד, גבוה = יותר יצירתי
    "repetition_penalty": 1.4, # עוזר למנוע לופים של אתרי חדשות
    "max_new_tokens": 300
}

print("דוכיפת מזרימה נתונים בזמן אמת...\n")

try:
    with requests.post(URL, json=payload, stream=True) as r:
        r.raise_for_status()
        for line in r.iter_lines():
            if line:
                decoded = line.decode('utf-8')
                if decoded.startswith("data: "):
                    # חילוץ הטוקן והדפסה מיידית
                    token = decoded.replace("data: ", "")
                    print(token, end="", flush=True)
except Exception as e:
    print(f"\nError: {e}")

print("\n" + "="*30)

Official App:

Go to: https://duchifat-2-474222140307.us-west1.run.app