lexi-coder-v4.2
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
| Parameters | 3,847,556,096 (3.85B) |
| Weights on disk | 7.15 GB |
| Trained context length | 2,048 tokens |
| Base model | microsoft/Phi-4-mini-instruct |
Approximate memory to hold the weights. Add context and runtime overhead on top.
| Precision | Weights |
|---|---|
| FP16 / BF16 | 7.17 GB |
| 8-bit (Q8_0) | 3.58 GB |
| 4-bit (Q4_K_M) | 1.97 GB |
Training
| Strategy | lora |
| Adapter | Auto LoRA |
| LoRA rank / alpha | 16 / 32 |
| Dataset | ianncity/GLM-5.2-Conversation |
| Samples learned | 50,296 (through phase 26 of 50) |
| Training steps | 1,480 |
| Epochs | 5 |
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("lexi-coder-v4.2")
tokenizer = AutoTokenizer.from_pretrained("lexi-coder-v4.2")
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:
ianncity/GLM-5.2-Conversation
Copyright (c) 2026 Reallexi LLC. All rights reserved.
Produced by Reallexi LLC AI Model Builder from training job #1562. Core: https://llm.reallexi.io
Keep reallexi-model.json, NOTICE, and all applicable upstream license files with the model.