LFM2.5-2.6B Fable5 Coding Agent
A lightweight coding-agent model fine-tuned from LFM2.5-2.6B on an agentic coding dataset, optimized for local inference.
Timeline
Completed fine-tuning and evaluation with local inference benchmarking.
Role & Scope
Prepared agentic coding dataset, executed fine-tuning, and validated inference behavior on coding tasks.
Team Setup
Individual model adaptation and evaluation workflow.
Context
Problem: Local coding assistance needed a lightweight instruction-following model with agentic behavior, rather than large general models.
Solution: Full fine-tuned LFM2.5-2.6B Fable5 on an agentic coding dataset, optimizing for local inference constraints while preserving instruction-following behavior.
Impact: Created a deployable lightweight coding-agent model suitable for local and resource-constrained environments.
Implementation Details
Architecture
LFM2.5-2.6B-based model fine-tuned for agentic coding behavior with instruction-following and tool-use patterns.
Data & Evaluation
Evaluated on coding-agent benchmarks and measured instruction adherence, tool-use correctness, and latency.
Production & Ops
Optimized for local inference deployment with quantization and lightweight serving paths.
Lessons Learned
Lightweight coding agents require careful balance between model size, instruction tuning, and tool-use training data.
Links
Last verified: September 1, 2026
Public Evidence
Last verified: September 1, 2026
This case study summarizes production work delivered under private repositories and confidentiality constraints. Public artifacts are intentionally omitted while keeping the implementation narrative transparent.