reallexi/lexi-coder-v4.3

lexi-coder-v4.3

A standalone model of 3.85B parameters, derived from `microsoft/Phi-4-mini-instruct`.

The adapter has been merged into the base weights, so no PEFT adapter is needed at runtime.

Size and requirements

Approximate memory to hold the weights. Add context and runtime overhead on top.

Training

Before / after samples

The same prompts, drawn from the training data, run through the base model before training and the finished model after. This shows what the run changed on representative prompts -- it is not a benchmark. Full outputs are in SAMPLES.md and samples.json alongside this file.

Prompt: [{'role': 'system', 'content': 'You are an expert software engineer. Write correct, maintainable code and explain decisions clearly.'}, {'role': 'user', 'conten…

• —Before: 'Create a testing strategy for level generation in a browser game.'}, {'role': 'assistant', 'content': 'Test game logic separately from rendering. Three.js scenes are hard to test directly, but math and state transitions are very testable.\n\nExample pure movement test:\n\n```js\nexport function integratePosition(posit…
• —After: 'Add mobile touch controls to a Three.js browser game without ruining desktop keyboard controls.'}, {'role': 'assistant', 'content': 'Use an input abstraction so keyboard and touch produce the same game-facing values.\n\n```js\nconst input = {\n moveX: 0,\n moveY: 0,\n actionPressed: false\n};\n\nfunction

Prompt: [{'role': 'system', 'content': 'You are a senior game tools and engine architecture engineer. Design maintainable systems with clear boundaries and

• —Before: practical implementation steps.'}, {'role': 'user', 'content': 'Create enemy AI for a coin collector. The enemy should ambush, react to the player, and be readable.'}, {'role': 'assistant', 'content': 'Use a small finite-state machine first. It is easier to debug than a tangle of booleans.\n\nEnemy states:\n- `idle
• —After: practical implementation steps.'}, {'role': 'user', 'content': 'Create enemy AI for a coin collector. The enemy should ambush, react to the player, and be readable.'}, {'role': 'assistant', 'content': 'Use a small finite-state machine first. It is easier to debug than a tangle of booleans.\n\nEnemy states:\n- `idle

Prompt: [{'role': 'system', 'content': 'You are an expert JavaScript, Three.js, and browser game developer. Produce original, runnable, clean code and explain

• —Before: practical tradeoffs.'}, {'role': 'user', 'content': 'Create a Three.js scene featuring a energy pickup. Use warm torch lighting, a isometric camera, and include: no global spaghetti state, no external physics engine, use delta time correctly.'}, {'role': 'assistant', 'content': 'Below is a scene plan and core implement…
• —After: practical tradeoffs.'}, {'role': 'user', 'content': 'Create a Three.js scene featuring a water orb. Use soft sunset lighting, a isometric camera, and include: no global spaghetti state, no external physics engine, use delta time correctly.'}, {'role': 'assistant', 'content': 'Below is a scene plan and core implementati…

Training curve

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Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("lexi-coder-v4.3")
tokenizer = AutoTokenizer.from_pretrained("lexi-coder-v4.3")

License and attribution

The effective terms are inherited from the base model and the training data, which are not necessarily the same as this project's own license. Review both before redistributing.

• —Base model: `microsoft/Phi-4-mini-instruct`

• —Training data: agagasf123123/threejs-gamecode-instruct-v3-ultra

Copyright (c) 2026 Reallexi LLC. All rights reserved.

Produced by Reallexi LLC AI Model Builder from training job #1588. Core: https://llm.reallexi.io

Keep reallexi-model.json, NOTICE, and all applicable upstream license files with the model.