.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:

pip install -U transformers accelerate

Quickstart

Here's a simple example demonstrating how to load and use the model:

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 file for complete details.

---

Developed by SVECTOR