105 lines
4.8 KiB
Markdown
105 lines
4.8 KiB
Markdown
---
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license: other
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license_name: "inherits-base-model-and-dataset-terms"
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base_model: "microsoft/Phi-4-mini-instruct"
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library_name: transformers
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pipeline_tag: "text-generation"
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tags:
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- "ai-model-builder"
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- "fine-tuned"
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- lora
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- reallexi
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- "text-generation"
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---
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# lexi-coder-v4.3
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A standalone model of 3.85B parameters, derived from [`microsoft/Phi-4-mini-instruct`](https://huggingface.co/microsoft/Phi-4-mini-instruct).
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The adapter has been merged into the base weights, so no PEFT adapter is needed at runtime.
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## Size and requirements
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| Parameters | 3,847,556,096 (3.85B) |
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| Weights on disk | 7.15 GB |
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| Trained context length | 15,360 tokens |
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| Base model | `microsoft/Phi-4-mini-instruct` |
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Approximate memory to hold the weights. Add context and runtime overhead on top.
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| Precision | Weights |
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|---|---|
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| FP16 / BF16 | 7.17 GB |
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| 8-bit (Q8_0) | 3.58 GB |
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| 4-bit (Q4_K_M) | 1.97 GB |
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## Training
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| Strategy | lora |
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| Adapter | Auto LoRA |
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| LoRA rank / alpha | 8 / 16 |
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| Dataset | `agagasf123123/threejs-gamecode-instruct-v3-ultra` |
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| Samples learned | 45,936 (through phase 10 of 20) |
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| Training steps | 1,170 |
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| Epochs | 5 |
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## Before / after samples
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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.
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**Prompt:** [{'role': 'system', 'content': 'You are an expert software engineer. Write correct, maintainable code and explain decisions clearly.'}, {'role': 'user', 'conten…
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- 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…
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- 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
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**Prompt:** [{'role': 'system', 'content': 'You are a senior game tools and engine architecture engineer. Design maintainable systems with clear boundaries and
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- 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
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- 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
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**Prompt:** [{'role': 'system', 'content': 'You are an expert JavaScript, Three.js, and browser game developer. Produce original, runnable, clean code and explain
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- 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…
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- 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…
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## Training curve
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("lexi-coder-v4.3")
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tokenizer = AutoTokenizer.from_pretrained("lexi-coder-v4.3")
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```
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## License and attribution
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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.
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- Base model: [`microsoft/Phi-4-mini-instruct`](https://huggingface.co/microsoft/Phi-4-mini-instruct)
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- Training data: `agagasf123123/threejs-gamecode-instruct-v3-ultra`
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Copyright (c) 2026 Reallexi LLC. All rights reserved.
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Produced by Reallexi LLC AI Model Builder from training job #1588.
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Core: https://llm.reallexi.io
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Keep `reallexi-model.json`, `NOTICE`, and all applicable upstream license files with the model.
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