---
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
tags:
- code
- text-generation
- text
- agent
---
# .dotcode-1-mini
## 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.
---
Developed by SVECTOR
