> For the complete documentation index, see [llms.txt](https://docs.cloud.olakrutrim.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.cloud.olakrutrim.com/basics/ai-studio/ai-jobs/fine-tuning.md).

# Fine-tuning

## Fine-Tuning

Fine-tuning allows you to adapt powerful pre-trained models to your own data and domain. This improves output quality, accuracy, and relevance for your specific use case.

AI Studio simplifies the fine-tuning process using efficient adapter-based methods, so you can achieve high-quality customization without the infrastructure complexity.

***

### 1. How Fine-Tuning Works on AI Studio

AI Studio supports fine-tuning via **LoRA (Low-Rank Adaptation)** adapters. These adapters:

* Do not alter the base model weights
* Are faster and cheaper to train
* Enable task-specific customization
* Allow easy rollback or switching between fine-tuned variants

All fine-tuning jobs are run on managed infrastructure with checkpointing and easy deployment built in.

***

### 2. Creating a Fine-Tuning Job

You can create a fine-tuning job from the **Fine-Tuning** section of the console.

#### Step-by-Step

1. **Select a Model**\
   Choose from supported models like Llama-3 or Mistral. Only supported models will appear in the dropdown.
2. **Upload a Dataset**\
   Format must follow the structure below (JSONL or JSON array):

   <pre class="language-json" data-overflow="wrap"><code class="lang-json">{
     "instruction": "Summarize the following text:",
     "input": "Artificial Intelligence is transforming industries...",
     "output": "AI is revolutionizing industries by automating tasks..."
   }
   </code></pre>
3. **Configure Training Parameters**
   * **LoRA Rank**: Controls capacity of the adapter. Higher rank = more detailed adaptation.
   * **Learning Rate**: Default is 1. Lower values are more conservative.
   * **Batch Size**: Default is 8. Tune based on GPU resources.
   * **Checkpointing**: Enable to resume from intermediate points or track progress.
4. **Launch Job**\
   Once configured, submit the job. Resources will be provisioned automatically.

***

### 3. Monitoring Progress

After launching, the console displays real-time metrics:

* Training loss
* Token processed
* Checkpoint status
* Remaining time

Checkpoints are saved periodically and can be used to resume or deploy at any stage.

***

### 4. Deploying the Fine-Tuned Model

Once training is complete:

1. Click **Deploy** from the job page
2. Choose a unique **deployment name**
3. The model will be deployed to a dedicated **NVIDIA H100 GPU** instance
4. Once deployed, it is accessible via the same API structure used for base models

You can manage or terminate deployments to control costs.

***

### 5. Best Practices for Dataset Preparation

* Use high-quality, representative examples
* Avoid noisy or inconsistent entries
* Ensure input-output alignment for each task
* Keep instruction phrasing consistent if possible
* Aim for at least a few hundred examples; more if doing complex generation

***

### 6. Supported Models

Currently supported for fine-tuning:

* **Llama-3-8B-Instruct**
* **Llama-3-8B**
* **Mistral-7B**

{% hint style="info" %}
More models will be added over time.
{% endhint %}

***

### 7. Pricing

Fine-tuning is billed per **1 million tokens processed** during training.

| Model               | Cost per 1M Tokens |
| ------------------- | ------------------ |
| Llama-3-8B-Instruct | ₹33                |

Deployment is billed per GPU-hour:

| Deployment Type  | GPU         | Cost per Hour |
| ---------------- | ----------- | ------------- |
| Fine-tuned Model | NVIDIA H100 | ₹215          |

For cost efficiency:

* Review your dataset before launch
* Use checkpointing to avoid reruns
* Shut down deployments when not in use

***

### 8. Next Steps

* Evaluate your fine-tuned model on benchmark tasks
* Deploy the model for production use
* Review Billing for usage-based pricing and limits
* Consult the API Reference to integrate your fine-tuned model


---

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