README.md

---
license: mit
language:
- en
base_model:
- LLMLit/LLMLit
tags:
- LitSeek
- Romania
- LLM
datasets:
- LLMLit/LitSet
metrics:
- accuracy
- character
- code_eval
---
Here is the improved and well-structured version of the LitSeek Model Card:  

---

# **LitSeek – Model Card**  
📌 *High-performance multilingual LLM for advanced NLP applications*  

🔗 [LitSeek on Hugging Face](https://huggingface.co/LLMLit/LitSeekR1)  
🔗 [LLMLit on Hugging Face](https://huggingface.co/LLMLit)  

---

## **🔍 Quick Summary**  
**LitSeek** is a cutting-edge multilingual **large language model (LLM)** fine-tuned from **LLMLit, DeepSeek, and Meta's Llama 3.1 8B Instruct model**. Designed primarily for **English NLP tasks**, LitSeek delivers **accurate, context-aware, and efficient** results, leveraging advanced **instruction-following** capabilities.  

---

## **📌 Model Details**  

### **📝 Model Description**  
LitSeek is optimized for a broad range of **Natural Language Processing (NLP) tasks**, including:  
✔️ **Content generation**  
✔️ **Summarization**  
✔️ **Question answering**  
✔️ **Translation** (English ↔ Romanian)  

With a strong emphasis on **high-quality instruction adherence** and **deep contextual understanding**, LitSeek is a powerful tool for **developers, researchers, and businesses** seeking advanced **NLP solutions**.  

| Feature | Details |
|---------|---------|
| 🏢 **Developed by**  | LLMLit Development Team |
| 💰 **Funded by**  | Open-source contributions & private sponsors |
| 🌍 **Languages**  | English (en), Romanian (ro) |
| 🏷 **License**  | MIT |
| 🔗 **Fine-tuned from**  | LLMLit, DeepSeek R1, Meta Llama-3.1-8B-Instruct |
| 📂 **Resources**  | [GitHub Repository](https://github.com/PyThaGoAI/LLMLit) / Paper: *To be published* |
| 🚀 **Demo**  | *Coming Soon* |

---

## **💡 Key Use Cases**  

### ✅ **Direct Applications**  
LitSeek can be directly applied to:  
- 📜 **Generating human-like text responses**  
- 🌍 **Translating between English and Romanian**  
- 📑 **Summarizing long-form content (articles, reports, documents, etc.)**  
- 🧠 **Answering complex queries with contextual awareness**  

### 🚀 **Advanced Use Cases (Fine-tuning & Integration)**  
When integrated into larger ecosystems, LitSeek can power:  
- 🤖 **Chatbots & virtual assistants**  
- 🎓 **Educational tools for multilingual environments**  
- ⚖️ **Legal & medical document analysis**  
- 🛍 **E-commerce & customer support automation**  

---

## **⚠️ Out-of-Scope Uses**  
LitSeek is **not** recommended for:  
❌ Malicious applications (e.g., misinformation, propaganda)  
❌ Critical decision-making without human oversight  
❌ Low-latency, real-time processing in constrained environments  

---

## **⚖️ Bias, Risks & Limitations**  

### **🔎 Bias**  
- Like all LLMs, **LitSeek may inherit biases** from its training data, reflecting **societal or cultural biases**.  

### **⚠️ Risks**  
- **Potential misuse** for generating misleading or harmful content.  
- **Inaccurate responses** in highly specialized or domain-specific queries.  

### **📉 Limitations**  
- Performance depends on **instruction clarity & input quality**.  
- Limited understanding of **niche or highly technical fields**.  

### **✅ Best Practices & Recommendations**  
- Always **review** generated content for accuracy.  
- **Fine-tune** or customize the model for **domain-specific** applications.  

---

## **🚀 Getting Started with LitSeek**  
To use LitSeek, install the necessary libraries and load the model as follows:  

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model = AutoModelForCausalLM.from_pretrained("LLMLit/LitSeekR1")
tokenizer = AutoTokenizer.from_pretrained("LLMLit/LitSeekR1")

# 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))
```

---

---

# **📌 Installation Guide: Ollama + LitSeek**  

## **🔹 Step 1: Install Ollama**  
Ollama is a lightweight framework for running large language models (LLMs) locally.  

### **🖥️ For macOS & Linux**  
1️⃣ **Open a terminal and run:**  
```sh
curl -fsSL https://ollama.com/install.sh | sh
```
2️⃣ **Restart your terminal.**  

### **🖥️ For Windows (WSL2 required)**  
1️⃣ **Enable WSL2 and install Ubuntu:**  
   - Open PowerShell as Administrator and run:  
   ```powershell
   wsl --install
   ```
   - Restart your computer.  

2️⃣ **Install Ollama inside WSL2:**  
   ```sh
   curl -fsSL https://ollama.com/install.sh | sh
   ```  

3️⃣ **Check if Ollama is installed correctly:**  
   ```sh
   ollama
   ```
   If it prints the usage instructions, the installation is successful. 🎉  

---

## **🔹 Step 2: Install LLMLit from Hugging Face**  
LLMLit can be downloaded and run inside Ollama using the `ollama pull` command.  

1️⃣ **Open a terminal and run:**  
```sh
ollama pull llmlit/LeetSeek-R1-DLlama-8B
```  

2️⃣ **Verify the installation:**  
```sh
ollama list
```  
You should see **LLMLit** in the list of installed models. ✅  

---

## **🔹 Step 3: Run LLMLit in Ollama**  
After installation, you can interact with **LLMLit** using:  

```sh
ollama run llmlit/LeetSeek-R1-DLlama-8B
```  
This starts a local session where you can chat with the model! 🤖  

For custom prompts:  
```sh
ollama run llmlit/LeetSeek-R1-DLlama-8B "Hello, how can I use LLMLit?"
```  

---

## **🔹 Bonus: Use LLMLit in Python**  
If you want to integrate **LLMLit** into a Python script, install the required library:  
```sh
pip install ollama
```  

Then, create a Python script:  
```python
import ollama

response = ollama.chat(model='llmlit/LeetSeek-R1-DLlama-8B', messages=[{'role': 'user', 'content': 'How does LLMLit work?'}])
print(response['message']['content'])
```  

---

🚀 **Done!** Now you have **Ollama + LLMLit** installed and ready to use locally!



🌟 Coming soon!

Themes and Agents.

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.

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 the user experience.


🌐 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.
![Civis3.gif](https://cristiansas.com/storage/agentweb-1.gif)

🖥️ LITflow
Low-Code Platform for Custom Apps: Litflow 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.

🗣️ 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.

🌍 Litchat
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.

🔧 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.

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. 🔥


![Civis3.png](https://cdn-uploads.huggingface.co/production/uploads/6769b18893c0c9156b8265d5/pZch1_YVa6Ixc3d_eYxBR.png)


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