--- base_model: bigcode/starcoder2-3b tags: - text-generation-inference - transformers - unsloth - starcoder2 license: bigcode-openrail-m language: - en --- # Lovelace-1-3B *A code-focused large language model for reliable, scalable software reasoning* --- ## Overview **Lovelace-1-3B** is a 3-billion parameter coding-centric language model built on top of the [`bigcode/starcoder2-3b`](https://huggingface.co/bigcode/starcoder2-3b) foundation model. It is designed as the first release in the **Lovelace** family: a line of models focused on **practical code generation, reasoning, and tooling**, with an emphasis on long-term scalability, research cleanliness, and deployment stability. Lovelace is developed with a research-first mindset: prioritising architectural soundness, future extensibility, and real-world usability over short-term leaderboard optimisation. --- ## Model Family | Model | Parameters | Status | | -------------- | ---------- | ----------- | | Lovelace-1-3B | 3B | ✅ Available | | Lovelace-1-7B | 7B | ✅ Available | | Lovelace-1-15B | 15B | 🚧 Planned | All models in the Lovelace family share a consistent design philosophy and are intended to be drop-in compatible with the **Lovelace Code** runtime and tooling stack. --- ## Design Philosophy Lovelace is guided by three core principles: 1. **Engineering realism** The model is expected to recognise infeasible requests, surface constraints clearly, and propose workable alternatives rather than hallucinating solutions. 2. **Scalability over spectacle** Training and design decisions prioritise long-term scale (larger models, longer contexts, multimodality) rather than short-term benchmark gains. 3. **Tool-aligned coding intelligence** Lovelace is designed to function as part of a broader coding system — not as an isolated chatbot. --- ## Lovelace Code Library The model is intended to be used alongside **Lovelace Code**, a companion library that provides: * Structured prompt interfaces for coding tasks * Execution-aware request handling * Support for long-running and multi-step code generation * Guardrails against unrealistic or non-computable requests > Ongoing work focuses on improving **stability for long requests**, including multi-file generation, extended reasoning chains, and iterative refinement workflows. --- ## Capabilities While formal benchmarks are not yet published, Lovelace-1-3B is trained and evaluated internally for: * Code generation and completion * Code explanation and refactoring * Debugging and error analysis * API and library usage reasoning * High-level system design discussion The model is particularly tuned to **respond sensibly under uncertainty**, favouring correctness and clarity over speculative output. --- ## Current Limitations * No public benchmark suite released yet * Context length stability for very long requests is still under active development * Vision-language capabilities are **not yet supported** These limitations are explicitly acknowledged and form part of the near-term roadmap. --- ## Roadmap Planned future work includes: * Improved long-context stability in Lovelace Code * Release of the **Lovelace-1-15B** model * Vision support (code + visual inputs) * Transparent evaluation and benchmark reporting * Deeper tool and execution integration --- ## Intended Use Lovelace is designed for: * Research and experimentation in code-focused LLMs * Developer tooling and agentic coding systems * Education and structured programming assistance It is **not** intended for safety-critical systems without further evaluation. --- ## Acknowledgements Lovelace-1-3B is based on the excellent work of the BigCode project, specifically [`starcoder2-3b`](https://huggingface.co/bigcode/starcoder2-3b). The project is inspired by modern research-grade model releases, including OpenAI’s open-weight efforts and contemporary large-scale coding systems. --- ## Licence Please refer to the underlying base model and repository for licensing details. Additional terms may apply to the Lovelace Code library.