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axolotl/docs/inference.qmd
NanoCode012 51cd409488 Feat: minor docs improvements for RLHF and faq on embeddings (#2401) [skip ci]
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* chore: rename inference doc

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* Update docs/getting-started.qmd

Co-authored-by: salman <salman.mohammadi@outlook.com>

* Update docs/rlhf.qmd

Co-authored-by: salman <salman.mohammadi@outlook.com>

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Co-authored-by: salman <salman.mohammadi@outlook.com>
2025-03-17 08:39:04 -04:00

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---
title: "Inference and Merging"
format:
html:
toc: true
toc-depth: 3
number-sections: true
execute:
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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).