PyThaGo
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LLMLit

@@ -16,272 +16,91 @@ metrics:
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-# Model Card for LLMLit
-## Quick Summary
-LLMLit is a high-performance, multilingual large language model (LLM) fine-tuned from Meta's Llama 3.1 8B Instruct model. Designed for both English and Romanian NLP tasks, LLMLit leverages advanced instruction-following capabilities to provide accurate, context-aware, and efficient results across diverse applications.
-## Model Details
-### Model Description
-LLMLit is tailored to handle a wide array of tasks, including content generation, summarization, question answering, and more, in both English and Romanian. The model is fine-tuned with a focus on high-quality instruction adherence and context understanding. It is a versatile tool for developers, researchers, and businesses seeking reliable NLP solutions.
-- **Developed by:** LLMLit Development Team
-- **Funded by:** Open-source contributions and private sponsors
-- **Shared by:** LLMLit Community
-- **Model type:** Large Language Model (Instruction-tuned)
-- **Languages:** English (en), Romanian (ro)
-- **License:** MIT
-- **Fine-tuned from model:** meta-llama/Llama-3.1-8B-Instruct
-### Model Sources
-- **Repository:** [GitHub Repository Link](https://github.com/PyThaGoAI/LLMLit)
-- **Paper:** [To be published]
-- **Demo:** [Coming Soon)
-## Uses
-### Direct Use
-LLMLit can be directly applied to tasks such as:
-- Generating human-like text responses
-- Translating between English and Romanian
-- Summarizing articles, reports, or documents
-- Answering complex questions with context sensitivity
-### Downstream Use
-When fine-tuned or integrated into larger ecosystems, LLMLit can be utilized for:
-- Chatbots and virtual assistants
-- Educational tools for bilingual environments
-- Legal or medical document analysis
-- E-commerce and customer support automation
-### Out-of-Scope Use
-LLMLit is not suitable for:
-- Malicious or unethical applications, such as spreading misinformation
-- Highly sensitive or critical decision-making without human oversight
-- Tasks requiring real-time, low-latency performance in constrained environments
-## Bias, Risks, and Limitations
-### Bias
-- LLMLit inherits biases present in the training data. It may produce outputs that reflect societal or cultural biases.
-### Risks
-- Misuse of the model could lead to misinformation or harm.
-- Inaccurate responses in complex or domain-specific queries.
-### Limitations
-- Performance is contingent on the quality of input instructions.
-- Limited understanding of niche or highly technical domains.
-### Recommendations
-- Always review model outputs for accuracy, especially in sensitive applications.
-- Fine-tune or customize for domain-specific tasks to minimize risks.
-## How to Get Started with the Model
-To use LLMLit, install the required libraries and load the model as follows:
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
-# Load the model and tokenizer
 model = AutoModelForCausalLM.from_pretrained("llmlit/LLMLit-0.2-8B-Instruct")
 tokenizer = AutoTokenizer.from_pretrained("llmlit/LLMLit-0.2-8B-Instruct")
-# Generate text
 inputs = tokenizer("Your prompt here", return_tensors="pt")
 outputs = model.generate(**inputs, max_length=100)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
-## Training Details
-### Training Data
-LLMLit is fine-tuned on a diverse dataset containing bilingual (English and Romanian) content, ensuring both linguistic accuracy and cultural relevance.
-### Training Procedure
-#### Preprocessing
-- Data was filtered for high-quality, instruction-based examples.
-- Augmentation techniques were used to balance linguistic domains.
-#### Training Hyperparameters
-- **Training regime:** Mixed precision (fp16)
-- **Batch size:** 512
-- **Epochs:** 3
-- **Learning rate:** 2e-5
-#### Speeds, Sizes, Times
-- **Checkpoint size:** ~16GB
-- **Training time:** Approx. 1 week on 8 A100 GPUs
-## Evaluation
-### Testing Data, Factors & Metrics
-#### Testing Data
-Evaluation was conducted on multilingual benchmarks, such as:
-- FLORES-101 (Translation accuracy)
-- HELM (Instruction-following capabilities)
-#### Factors
-Evaluation considered:
-- Linguistic fluency
-- Instruction adherence
-- Contextual understanding
-#### Metrics
-- BLEU for translation tasks
-- ROUGE-L for summarization
-- Human evaluation scores for instruction tasks
-### Results
-LLMLit achieves state-of-the-art performance on instruction-following tasks for English and Romanian, with BLEU scores surpassing comparable models.
-#### Summary
-LLMLit excels in bilingual NLP tasks, offering robust performance across diverse domains while maintaining instruction adherence and linguistic accuracy.
-## Model Examination
-Efforts to interpret the model include:
-- Attention visualization
-- Prompt engineering guides
-- Bias audits
-## Environmental Impact
-Training LLMLit resulted in estimated emissions of ~200 kg CO2eq. Carbon offsets were purchased to mitigate environmental impact. Future optimizations aim to reduce energy consumption.
-LLMLit – The Cutting-Edge AI Assistant for Performance Analysis and Prediction
-LLMLIT – Coming Soon
-The premium version of the LLama 3 model, LLMLIT, sets a new standard in AI through advanced customization and innovative technologies, offering ideal solutions for a wide range of industries and applications.
-This version integrates Retrieval-Augmented Generation (RAG) capabilities, ensuring fast and accurate access to relevant and specialized information.
-Key Features of LLMLIT:
-Advanced Customization: Tailored to meet the specific needs of each user, delivering optimized solutions.
-Enhanced RAG Integration: Support for multiple domains and complex data sources.
-Innovative Frontend and Backend:Frontend: Intuitive, customizable interfaces with user-centric interactions.
-Backend: Top-tier performance, rapid data processing, and efficient task management.
-Extensive Community Integrations
-LLMLIT supports a wide range of platforms and applications, offering unparalleled flexibility:
-Web & Desktop: Open WebUI, HTML UI, Ollama GUI, LMstudio, MindMac, Ollama Spring.
-Mobile: Native apps such as Enchanted, macAI, Ollama Telegram Bot, and Ollama RAG Chatbot.
-CLI & Terminal: Advanced plugins for Emacs, Vim, and tools like ShellOracle and typechat-cli.
-Extensions & Plugins: Raycast Extensions, Obsidian Plugins, Ollama for Discord, and more.
-Package Managers: Integration with Pacman, Gentoo, Nix, and Flox.
-Enterprise Solutions & Advanced AI
-AI Frameworks and Chatbot UI: Hollama, Saddle, big-AGI, Cheshire Cat, Amica.
-Backend RAG Integration: LangChain, LangChainGo, Haystack, and Semantic Kernel.
-Developer Support: VSCode extensions, QodeAssist for Qt Creator, and Ollama support for multiple programming languages (Java, Python, C++, etc.).
-Team and Multi-Agent Applications: AnythingLLM, crewAI, and BrainSoup.
-Cross-Platform Performance
-LLMLIT delivers advanced interoperability:
-MacOS Native: OllamaSwift, macAI, and support for Apple Vision Pro.
-Windows/Linux: Docker-native and containerized apps like ARGO and StreamDeploy.
-Mobile Applications: Ollama Telegram Bot, Ollama Discord Bot, and Ollama RAG Chatbot.
-Open Ecosystem: Integration with popular platforms such as Google Mesop, Firebase, and SAP ABAP.
-The Future of AI is Here 🚀
-LLMLIT revolutionizes how we work with large language models, offering a scalable, powerful, and adaptable platform ready to meet the most demanding needs with innovation, flexibility, and superior performance.
-🌟 Themes and Agents: Revolutionizing AI-driven Applications
-The integration of AI-powered technologies into development tools is rapidly transforming how applications are built and deployed. With LLMLit as the core engine, this suite of tools offers groundbreaking possibilities, from low-code app building to advanced conversational agents.
-AI-Driven Development in Your Terminal 🚀
-Design full-stack web applications with AI-powered capabilities directly from your terminal. This environment is built for large, real-world tasks, allowing developers to prompt, run, edit, and deploy web apps with seamless integration into your workflow.
-Low-Code App Builder for RAG and Multi-Agent AI Applications 🔧
-Python-based and agnostic to any model, API, or database, this platform simplifies the development of complex AI-driven applications, including Retrieval-Augmented Generation (RAG) and multi-agent AI systems.
-It empowers developers to create powerful apps without needing extensive coding knowledge, making it ideal for businesses and researchers who want to implement sophisticated AI without the overhead.
-Open Voice OS and HiveMind Installer 🔊
-Ansible-based: Set up and configure your Open Voice OS and HiveMind installer effortlessly with an intuitive, text-based user interface.
-This provides an easy installation and setup process, especially designed for voice-enabled applications and environments that rely on multi-agent systems.
-Generative UI: AI-Powered Search Engine 🔍
-Harness the power of a generative UI for your search engines. This AI-powered tool offers contextual searches and adaptive results, providing users with an efficient and intelligent way to explore content and data. It can be embedded in various systems like websites or apps to improve user experience.
-🔧 LitSeek-R1: Distilled Version
-A lighter, distilled version of the powerful LLMLit model, LitSeek-R1 maintains the same robust capabilities but with optimized performance for faster, more efficient responses. Perfect for applications requiring speed and low-latency operations.
-Agent and Application Themes
-🌐 LitAgentWeb-ui
-Direct Interaction with LLMLit: No complex installations required! This theme allows users to interact with LLMLit through a simple, intuitive web interface, making it ideal for applications that need to be accessed directly from a browser.
-Whether you're building a customer support system or a virtual assistant, AgentWeb-ui provides a fast and simple experience.
-💬 LibreChat
-Multi-AI Integration: LibreChat is a powerful chatbot that integrates multiple AI models, including LLMLit. It provides a natural conversational experience, whether for customer support or virtual assistance.
-Its flexibility and scalability make it suitable for a wide range of applications, helping businesses manage automated communication at scale.
-🖥️ LITflow
-Low-Code Platform for Custom Apps: Langflow is a low-code solution for creating custom applications that integrate seamlessly with LLMLit.
-It excels in building RAG-based applications, combining search and content generation to deliver smarter, faster solutions for complex environments. It's perfect for anyone looking to integrate advanced AI into their applications without the complexity of traditional development.
-📱 NextChat
-Cross-Platform Conversational App: NextChat is a fast, easy-to-use application that enables smooth, interactive conversations across different platforms.
-By integrating LLMLit, NextChat offers a dynamic interactive experience, ideal for users who want to have effective conversations with AI, whether for personal use, business, or service centers.
-🗣️ VoiceLit
-Voice Interaction Capabilities: Extend LLMLit’s abilities into the voice realm with VoiceLit. This extension brings AI-driven voice support to your applications, whether they’re for personal assistants or service centers.
-It enhances accessibility and interactivity, making it essential for creating voice-enabled AI applications.
-🌍 Web-llm-chat
-Run LLMLit Directly in the Browser: With Web-llm-chat, users can run LLMLit directly in their browser, bypassing the need for servers.
-This ensures maximum privacy and speed, offering a confidential and fast interaction experience. It’s perfect for applications where confidentiality and performance are of utmost importance.
-The Future of AI Interaction 🌐💡
-These themes and agents open up a wide array of possibilities, allowing businesses, developers, and individuals to easily integrate LLMLit into their systems. Whether it's building a simple chatbot or a highly sophisticated voice-enabled app, LLMLit offers the flexibility and power to transform the way we interact with AI technology. 🔥

 - character
 - code_eval
 ---
+Am reformulat și aranjat textul pentru a fi mai clar, bine structurat și ușor de citit. Vezi varianta îmbunătățită mai jos:
+---
+# **LLMLit – Model Card**
+📌 *High-performance multilingual LLM for English & Romanian NLP tasks*
+🔗 [LLMLit on Hugging Face](https://huggingface.co/LLMLit)
+🔗 [LitSeekR1 on Hugging Face](https://huggingface.co/LLMLit/LitSeekR1)
+---
+## **🔍 Quick Summary**
+**LLMLit** este un model de limbaj mare (LLM) performant, multilingv, optimizat din **Meta’s Llama 3.1 8B Instruct**. Este conceput pentru **task-uri NLP în limba engleză și română**, având capacități avansate de **urmărire a instrucțiunilor, înțelegere contextuală și generare de conținut precis**.
+## **📌 Model Details**
+🔹 **Descriere:** LLMLit poate fi utilizat pentru **generare de conținut, sumarizare, răspuns la întrebări și multe altele**.
+🔹 **Fine-tuning:** Modelul a fost antrenat pentru **adherarea la instrucțiuni de înaltă calitate și o mai bună înțelegere a contextului**.
+🔹 **Utilizatori țintă:** Dezvoltatori, cercetători și companii care au nevoie de **soluții NLP fiabile**.
+| Caracteristici  | Detalii |
+|----------------|---------|
+| 🏢 **Dezvoltat de**  | LLMLit Development Team |
+| 💰 **Finanțare**  | Contribuții open-source & sponsori privați |
+| 🌍 **Limbaje**  | Engleză (en), Română (ro) |
+| 🏷 **Licență**  | MIT |
+| 🔗 **Model de bază**  | `meta-llama/Llama-3.1-8B-Instruct` |
+| 📂 **Resurse**  | [GitHub Repository](#) / Paper: *To be published* |
+| 🚀 **Demo**  | *Coming Soon* |
+---
+## **💡 Utilizări principale**
+### ✅ **Utilizare directă**
+LLMLit poate fi aplicat la:
+✔️ Generarea de răspunsuri asemănătoare celor umane
+✔️ Traducere între **engleză și română**
+✔️ Sumarizarea articolelor, rapoartelor și documentelor
+✔️ Răspuns la întrebări complexe cu sensibilitate la context
+### 🚀 **Utilizare avansată (fine-tuning & integrare)**
+LLMLit poate fi optimizat pentru:
+🗨️ **Chatboți & asistenți virtuali**
+📚 **Instrumente educaționale bilingve**
+⚖️ **Analiza documentelor legale/medicale**
+🛒 **Automatizare în e-commerce & suport clienți**
+### ❌ **Utilizări nerecomandate**
+⛔ Aplicații neetice (dezinformare, manipulare)
+⛔ Luarea deciziilor critice fără supervizare umană
+⛔ Task-uri care necesită **performanță în timp real**
+---
+## **⚠️ Bias, Riscuri și Limitări**
+🔍 **Bias:** Modelul poate reflecta bias-urile existente în datele de antrenament.
+⚠️ **Riscuri:** Poate genera informații inexacte sau neconforme.
+📌 **Limitări:**
+- Performanța depinde de **calitatea prompturilor**.
+- Înțelegere limitată a domeniilor **foarte tehnice sau de nișă**.
+🔹 **Recomandări:**
+✔️ Revizuirea output-ului pentru **aplicații sensibile**.
+✔️ Fine-tuning pentru sarcini specifice pentru **minimizarea riscurilor**.
+---
+## **🚀 Cum să începi cu LLMLit**
+Pentru a utiliza LLMLit, instalează librăriile necesare și încarcă modelul:
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
+# Încarcă modelul și tokenizer-ul
 model = AutoModelForCausalLM.from_pretrained("llmlit/LLMLit-0.2-8B-Instruct")
 tokenizer = AutoTokenizer.from_pretrained("llmlit/LLMLit-0.2-8B-Instruct")
+# Generează text
 inputs = tokenizer("Your prompt here", return_tensors="pt")
 outputs = model.generate(**inputs, max_length=100)
 ```
+---
+🔗 **Mai multe detalii:** [LLMLit on Hugging Face](https://huggingface.co/LLMLit) 🚀