---
title: "Inference and Merging"
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---

This guide covers how to use your trained models for inference, including model loading, interactive testing, merging adapters, and common troubleshooting steps.

## Quick Start {#sec-quickstart}

::: {.callout-tip}
Use the same config used for training on inference/merging.
:::

### Basic Inference {#sec-basic}

::: {.panel-tabset}

## LoRA Models

```{.bash}
axolotl inference your_config.yml --lora-model-dir="./lora-output-dir"
```

## Full Fine-tuned Models

```{.bash}
axolotl inference your_config.yml --base-model="./completed-model"
```

:::

## Advanced Usage {#sec-advanced}

### Gradio Interface {#sec-gradio}

Launch an interactive web interface:

```{.bash}
axolotl inference your_config.yml --gradio
```

### File-based Prompts {#sec-file-prompts}

Process prompts from a text file:

```{.bash}
cat /tmp/prompt.txt | axolotl inference your_config.yml \
  --base-model="./completed-model" --prompter=None
```

### Memory Optimization {#sec-memory}

For large models or limited memory:

```{.bash}
axolotl inference your_config.yml --load-in-8bit=True
```

## Merging LoRA Weights {#sec-merging}

Merge LoRA adapters with the base model:

```{.bash}
axolotl merge-lora your_config.yml --lora-model-dir="./completed-model"
```

### Memory Management for Merging {#sec-memory-management}

::: {.panel-tabset}

## Configuration Options

```{.yaml}
gpu_memory_limit: 20GiB  # Adjust based on your GPU
lora_on_cpu: true        # Process on CPU if needed
```

## Force CPU Merging

```{.bash}
CUDA_VISIBLE_DEVICES="" axolotl merge-lora ...
```

:::

## Tokenization {#sec-tokenization}

### Common Issues {#sec-tokenization-issues}

::: {.callout-warning}
Tokenization mismatches between training and inference are a common source of problems.
:::

To debug:

1. Check training tokenization:
```{.bash}
axolotl preprocess your_config.yml --debug
```

2. Verify inference tokenization by decoding tokens before model input

3. Compare token IDs between training and inference

### Special Tokens {#sec-special-tokens}

Configure special tokens in your YAML:

```{.yaml}
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"
tokens:
  - "<|im_start|>"
  - "<|im_end|>"
```

## Troubleshooting {#sec-troubleshooting}

### Common Problems {#sec-common-problems}

::: {.panel-tabset}

## Memory Issues

- Use 8-bit loading
- Reduce batch sizes
- Try CPU offloading

## Token Issues

- Verify special tokens
- Check tokenizer settings
- Compare training and inference preprocessing

## Performance Issues

- Verify model loading
- Check prompt formatting
- Ensure temperature/sampling settings

:::

For more details, see our [debugging guide](debugging.qmd).