---
license: other
license_name: "inherits-base-model-and-dataset-terms"
base_model: "Qwen/Qwen2.5-0.5B-Instruct"
library_name: transformers
pipeline_tag: "text-generation"
tags:
- "ai-model-builder"
- "fine-tuned"
- reallexi
- slm
- "text-generation"
---

# reallexi/lexi-rm-agent

A standalone model of 495M parameters, derived from [`Qwen/Qwen2.5-0.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct).

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


## Size and requirements

| | |
|---|---|
| Parameters | 495,114,112 (495M) |
| Weights on disk | 953 MB |
| Trained context length | 512 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 |


## Training

| | |
|---|---|
| Strategy | slm |
| Adapter | Auto LoRA |
| LoRA rank / alpha | 8 / 16 |
| Dataset | `bitext/Bitext-customer-support-llm-chatbot-training-dataset` |
| Samples learned | 100,000 (through phase 382 of 382) |
| Training steps | 1,250 |
| Epochs | 5 |


## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("reallexi/lexi-rm-agent")
tokenizer = AutoTokenizer.from_pretrained("reallexi/lexi-rm-agent")
```


## 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: [`Qwen/Qwen2.5-0.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)

- Training data: `bitext/Bitext-customer-support-llm-chatbot-training-dataset`


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


Produced by Reallexi LLC AI Model Builder from training job #1272.
Core: https://llm.reallexi.io