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README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- code
+- text-generation
+---
+
+# .dotcode-1-mini
+
+<div align="left" style="line-height: 1;">
+  <a href="https://spec-chat.tech" target="_blank" style="margin: 2px;">
+    <img alt="SVECTOR Corporation" src="https://img.shields.io/badge/💬%20Spec%20Chat-Spec%20Chat-blue?style=plastic" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  
+  <a href="https://huggingface.co/SVECTOR-CORPORATION" target="_blank" style="margin: 2px;">
+    <img alt="SVECTOR Corporation" src="https://img.shields.io/badge/🤗%20Hugging%20Face-SVECTOR%20Corporation-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  
+  <a href="https://huggingface.co/SVECTOR-CORPORATION/dotcode-1-mini/blob/main/LICENSE" style="margin: 2px;">
+    <img alt="License" src="https://img.shields.io/badge/License-Apache%202.0-blue?color=1e88e5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+## Introduction
+
+We are excited to present **.dotcode-1-mini**, a compact and efficient language model developed by SVECTOR. This model represents our commitment to building accessible, high-performance AI solutions that empower developers and researchers.
+
+**.dotcode-1-mini** is designed to deliver:
+
+- **Efficiency:** Optimized architecture for fast inference and reduced computational requirements
+- **Versatility:** Strong performance across diverse text generation and code-related tasks
+- **Accessibility:** Open-source model available to the community under Apache 2.0 license
+
+Balanced approach to capability and resource efficiency.
+
+### Model Specifications
+
+- **Type:** Causal language model (LLaMA-based architecture)
+- **License:** Apache 2.0
+- **Context Length:** 32K
+
+## Requirements
+
+To use .dotcode-1-mini, ensure you have the latest versions of `transformers` and `accelerate` installed:
+
+```bash
+pip install -U transformers accelerate
+```
+
+## Quickstart
+
+Here's a simple example demonstrating how to load and use the model:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "SVECTOR-CORPORATION/dotcode-1-mini"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, 
+    torch_dtype=torch.bfloat16, 
+    device_map="auto", 
+    trust_remote_code=True
+)
+
+# Example prompt
+prompt = "Write a Python function to calculate fibonacci numbers:"
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    temperature=0.7,
+    top_p=0.9,
+    do_sample=True
+)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+## Use Cases
+
+.dotcode-1-mini excels at various tasks including:
+
+- **Code Generation:** Writing functions, scripts, and complete programs
+- **Text Completion:** Intelligent continuation of text and code
+- **Problem Solving:** Logical reasoning and algorithmic thinking
+- **Documentation:** Generating comments, docstrings, and technical explanations
+- **General Text Generation:** Creative writing, summaries, and content creation
+
+## Performance
+
+.dotcode-1-mini has been designed to provide strong performance while maintaining a compact model size. Detailed benchmarks and evaluation results will be shared as they become available.
+
+## Model Architecture
+
+Built on the LLaMA architecture, .dotcode-1-mini incorporates optimizations specifically tailored for:
+- Efficient token processing
+- Reduced memory footprint
+- Fast inference speeds
+- Balanced precision and performance
+
+## Training
+
+.dotcode-1-mini was trained on a diverse corpus including:
+- High-quality code repositories
+- Technical documentation
+- General text data
+- Curated datasets for improved reasoning
+
+*Detailed training methodology and data composition will be documented in future releases.*
+
+## Limitations
+
+As with any language model, .dotcode-1-mini has certain limitations:
+
+- May generate incorrect or outdated information
+- Performance varies based on prompt quality and task complexity
+- Not specifically fine-tuned for specialized domains without additional training
+- Should be used with appropriate safeguards in production environments
+
+## Ethical Considerations
+
+SVECTOR is committed to responsible AI development. Users should:
+
+- Review outputs for accuracy and appropriateness
+- Implement content filtering for sensitive applications
+- Avoid using the model for harmful or malicious purposes
+- Respect copyright and intellectual property when generating code
+
+## License
+
+This model is released under the Apache License 2.0. See the [LICENSE](https://huggingface.co/SVECTOR-CORPORATION/dotcode-1-mini/blob/main/LICENSE) file for complete details.
+
+---
+
+<p align="center">
+    <i>Developed by <a href="https://www.svector.co.in"> SVECTOR </a></i>
+</p>