Update README.md

README.md

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----
-license: mit
----
+---
+license: llama3.2
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- Algorithm
+- Coder
+- Llama
+---
+# **Llama-3.2-6B-AlgoCode**
+
+**Llama-3.2-6B-AlgoCode** is a collection of code-centric, multilingual large language models (LLMs) designed for text generation tasks involving algorithms and coding use cases. Available in both **1B** and **3B** parameter sizes, these models are pretrained and instruction-tuned for diverse generative tasks, particularly optimized for multilingual dialogue, agentic retrieval, and summarization.
+
+## Key Features
+
+- **Multilingual Support**: The models are optimized for generating text in multiple languages, making them ideal for multilingual coding environments.
+- **Instruction-Tuned**: Specially fine-tuned for instruction-following tasks to improve accuracy in complex generative workflows.
+- **Text-Only Models**: Focused entirely on text input and output, suitable for code generation, algorithmic problem-solving, summarization, and retrieval tasks.
+- **Agentic Retrieval**: Performs well in scenarios requiring retrieval-based responses and summarization of external knowledge.
+
+---
+
+## Intended Use
+
+Llama-3.2-6B-AlgoCode can be integrated using the Hugging Face `transformers` library for various text generation tasks:
+
+### Example Usage
+
+```python
+import torch
+from transformers import pipeline
+
+# Model ID from Hugging Face
+model_id = "prithivMLmods/Llama-3.2-6B-AlgoCode"
+
+# Initialize pipeline for text generation
+pipe = pipeline(
+    "text-generation", 
+    model=model_id, 
+    torch_dtype=torch.bfloat16, 
+    device_map="auto"
+)
+
+# Generate text
+response = pipe("The key to life is")
+print(response[0]['generated_text'])
+```
+
+---
+
+## Limitations
+
+### 1. **Bias and Fairness**
+Despite extensive training and alignment efforts, the model may still reflect biases inherent in the data it was trained on. Users should critically evaluate outputs, particularly in sensitive or high-impact contexts.
+
+### 2. **Contextual Understanding**
+While generally robust, the model may misinterpret complex or ambiguous prompts, resulting in inaccurate or irrelevant responses.
+
+### 3. **Real-Time Knowledge**
+The model’s knowledge is static, based on the data available during training. It does not include real-time information or updates on recent events and developments.
+
+### 4. **Safety and Harmlessness**
+Although the model is aligned with safety guidelines, there is a possibility of inappropriate or harmful outputs in certain contexts. It is recommended to employ human oversight and continuous monitoring when deploying the model in sensitive applications.
+
+### 5. **Resource Requirements**
+Running Llama-3.2-6B-AlgoCode efficiently requires substantial computational resources, especially for real-time or large-scale deployments. Leveraging GPUs with sufficient memory (16GB+) is recommended for optimal performance.
+
+### 6. **Ethical Considerations**
+Users must adhere to ethical guidelines when deploying this model. It should not be used for:
+- Generating harmful or malicious content
+- Spreading misinformation or spam
+- Any form of unethical activity
+
+### 7. **Domain-Specific Limitations**
+While the model excels in general-purpose text generation, it may require further fine-tuning for niche or highly specialized fields such as:
+- Medical
+- Legal
+- Financial
+
+---
+
+## Citation
+
+If you use Llama-3.2-6B-AlgoCode in your research or applications, please cite the model appropriately.
+
+```bibtex
+@misc{Llama3.2AlgoCode,
+  title = {Llama-3.2-6B-AlgoCode: Multilingual Code-Centric Large Language Models},
+  author = {PrithivMLMods},
+  year = {2025},
+  url = {https://huggingface.co/prithivMLmods/Llama-3.2-6B-AlgoCode}
+}
+```