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--- |
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license: llama3.2 |
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language: |
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- en |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- Algorithm |
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- Coder |
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- Llama |
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--- |
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# **Llama-3.2-6B-AlgoCode** |
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**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. |
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## Key Features |
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- **Multilingual Support**: The models are optimized for generating text in multiple languages, making them ideal for multilingual coding environments. |
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- **Instruction-Tuned**: Specially fine-tuned for instruction-following tasks to improve accuracy in complex generative workflows. |
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- **Text-Only Models**: Focused entirely on text input and output, suitable for code generation, algorithmic problem-solving, summarization, and retrieval tasks. |
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- **Agentic Retrieval**: Performs well in scenarios requiring retrieval-based responses and summarization of external knowledge. |
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--- |
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## Intended Use |
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Llama-3.2-6B-AlgoCode can be integrated using the Hugging Face `transformers` library for various text generation tasks: |
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### Example Usage |
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```python |
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import torch |
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from transformers import pipeline |
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# Model ID from Hugging Face |
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model_id = "prithivMLmods/Llama-3.2-6B-AlgoCode" |
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# Initialize pipeline for text generation |
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pipe = pipeline( |
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"text-generation", |
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model=model_id, |
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torch_dtype=torch.bfloat16, |
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device_map="auto" |
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) |
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# Generate text |
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response = pipe("The key to life is") |
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print(response[0]['generated_text']) |
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``` |
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--- |
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## Limitations |
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### 1. **Bias and Fairness** |
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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. |
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### 2. **Contextual Understanding** |
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While generally robust, the model may misinterpret complex or ambiguous prompts, resulting in inaccurate or irrelevant responses. |
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### 3. **Real-Time Knowledge** |
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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. |
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### 4. **Safety and Harmlessness** |
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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. |
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### 5. **Resource Requirements** |
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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. |
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### 6. **Ethical Considerations** |
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Users must adhere to ethical guidelines when deploying this model. It should not be used for: |
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- Generating harmful or malicious content |
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- Spreading misinformation or spam |
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- Any form of unethical activity |
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### 7. **Domain-Specific Limitations** |
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While the model excels in general-purpose text generation, it may require further fine-tuning for niche or highly specialized fields such as: |
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- Medical |
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- Legal |
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- Financial |
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