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reallexi/lexi-coder-v2-slm
lexi-coder-v2-slm is a text generation model from reallexi. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as other.
A standalone model of 495M parameters, derived from Qwen/Qwen2.5-0.5B-Instruct.
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From the Hugging Face model README
A standalone model of 495M parameters, derived from Qwen/Qwen2.5-0.5B-Instruct.
The adapter has been merged into the base weights, so no PEFT adapter is needed at runtime.
| Parameters | 495,114,112 (495M) |
| Weights on disk | 953 MB |
| Trained context length | 1,024 tokens |
| Base model | Qwen/Qwen2.5-0.5B-Instruct |
Approximate memory to hold the weights. Add context and runtime overhead on top.
| Precision | Weights |
|---|---|
| FP16 / BF16 | 944 MB |
| 8-bit (Q8_0) | 472 MB |
| 4-bit (Q4_K_M) | 260 MB |
| Strategy | llm |
| Adapter | Auto LoRA |
| Dataset | databricks/databricks-dolly-15k |
| Samples learned | 10,000 (through phase 10 of 10) |
| Training steps | 750 |
| Epochs | 3 |
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("reallexi/lexi-coder-v2-slm")
tokenizer = AutoTokenizer.from_pretrained("reallexi/lexi-coder-v2-slm")
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: Qwen/Qwen2.5-0.5B-Instruct
Training data: databricks/databricks-dolly-15k
Copyright (c) 2026 Reallexi LLC. All rights reserved.
Produced by Reallexi LLC AI Model Builder from training job #1369. Core: https://llm.reallexi.io