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SBD
SBD is an independent AI research and development project focused on building practical, accessible language models for coding, conversation, and software development.
Our goal is to experiment with smaller, efficient models that can run locally while still providing useful capabilities for developers and everyday users.
π€ Models
SBD4.5
SBD4.5 is a ~3B parameter coding and conversational language model.
It is designed for:
- π» Code generation
- π Debugging
- π¦ Rust programming
- π¬ General conversation
- π Code explanation
- π§ Software-development assistance
- β¨ Code completion
- π§ Technical question answering
π― Goals
SBD focuses on:
- Creating capable small language models
- Improving coding assistance
- Making models easier to run locally
- Supporting open model formats such as GGUF
- Experimenting with efficient training and fine-tuning
- Building models that work well with tools such as LM Studio and llama.cpp
- Making AI development more accessible
π§ͺ Research & Development
Models published by SBD may be experimental.
We regularly test:
- New datasets
- Training strategies
- Fine-tuning techniques
- Quantization methods
- Prompt formats
- Coding performance
- Local inference
Because of this, capabilities may differ between model versions.
π¦ Model Formats
Depending on the release, SBD models may be available in:
- Hugging Face Transformers / SafeTensors
- GGUF
- Quantized GGUF variants
- Full-precision checkpoints
Common GGUF releases may include:
Q8_0Q6_KQ5_K_MQ4_K_M
π Datasets
SBD experiments with coding and conversational datasets.
SBD4.5 uses coding data including:
Convence/Rust-Coder
https://huggingface.co/datasets/Convence/Rust-Coder
Additional datasets may be used for future model versions.
π Open Source
We aim to make SBD models easy to experiment with and run locally.
Unless stated otherwise on an individual model page, SBD models are released under permissive open-source licenses.
For example, SBD4.5 is released under Apache License 2.0.
Always check the individual model card for the exact license and usage conditions.
β οΈ Experimental Models
SBD models can produce:
- Incorrect information
- Non-compiling code
- Bugs
- Hallucinated APIs
- Insecure code
- Unexpected responses
Generated code should always be reviewed and tested before being used in production.
π SBD Model Series
The SBD model family represents our ongoing experimentation with local and developer-focused AI.
Current generation:
SBD4.5 β 3B Coding & Chat Model
More models, datasets, quantizations, and tools may be released as the project develops.
Build. Experiment. Improve.
SBD