> ## Documentation Index
> Fetch the complete documentation index at: https://docs.oxen.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Fine-Tuning text generation model

> Example of how to fine-tune text generation models on Oxen.ai using the HTTP API and `curl`.

## End-to-End Fine-Tuning of a Text Generation Model

This page walks through a complete fine-tuning run using the HTTP API and `curl`, mirroring the minimal Python example you provided.

You will:

* **Create** a fine-tune
* **Start** the fine-tune run
* **Monitor** the fine-tune until it completes

All calls are made against a specific repository, similar to this Python snippet:

* `NAMESPACE = "Tutorials"`
* `REPO = "FinancialSentiment"`

***

## Prerequisites

* **Repository** on Oxen with your training data committed, for example:
  * Namespace: `Tutorials`
  * Repository: `FinancialSentiment`
* **Dataset resource** inside that repo, for example:
  * `main/train_financial_sentiment.parquet`
  * You can view this example dataset (including columns like `text` and `sentiment`) and its repository here: [Tutorials / FinancialSentiment dataset](https://www.oxen.ai/Tutorials/FinancialSentiment/file/main/train_financial_sentiment.parquet)
* **API key** with access to the repo:
  * Exported as `OXEN_API_KEY`
* **Base URL** for the Oxen API:
  * Local dev example: `https://hub.oxen.ai`
  * Exported as `OXEN_BASE_URL` (optional, defaults shown below)

You can set these in your shell:

```bash theme={null}
export OXEN_API_KEY="YOUR_API_KEY_HERE"
export OXEN_BASE_URL="https://hub.oxen.ai"
export OXEN_NAMESPACE="Tutorials"
export OXEN_REPO="FinancialSentiment"
```

For the examples below, we will use:

* `resource`: `main/train_financial_sentiment.parquet`
* `base_model`: `Qwen/Qwen3-0.6B`
* `script_type`: `text_generation`

Training parameters mirror the Python example:

* `question_column`: `text`
* `answer_column`: `sentiment`
* `epochs`: `1`
* `batch_size`: `1`
* `learning_rate`: `0.0001`
* `grad_accum`: `1`
* `lora_alpha`: `16`
* `lora_rank`: `16`
* `seq_length`: `1024`
* `logging_steps`: `10`
* `enable_thinking`: `false`
* `neftune_noise_alpha`: `0.0`
* `save_steps_ratio`: `0.25`
* `save_strategy`: `epoch`
* `use_lora`: `true`

***

## Step 1 – Create a Fine-Tune

**Endpoint**

* `POST /api/repos/{owner}/{repo}/fine_tunes`

**Example `curl` request** (mirrors the Python payload):

```bash theme={null}
curl --location "${OXEN_BASE_URL:-https://hub.oxen.ai}/api/repos/${OXEN_NAMESPACE:-Tutorials}/${OXEN_REPO:-FinancialSentiment}/fine_tunes" \
  -H "Authorization: Bearer ${OXEN_API_KEY}" \
  -H "Content-Type: application/json" \
  --data '{
    "resource": "main/train_financial_sentiment.parquet",
    "base_model": "Qwen/Qwen3-0.6B",
    "script_type": "text_generation",
    "training_params": {
      "question_column": "text",
      "answer_column": "sentiment",
      "epochs": 1,
      "batch_size": 1,
      "learning_rate": 0.0001,
      "grad_accum": 1,
      "lora_alpha": 16,
      "lora_rank": 16,
      "seq_length": 1024,
      "logging_steps": 10,
      "enable_thinking": false,
      "neftune_noise_alpha": 0.0,
      "save_steps_ratio": 0.25,
      "save_strategy": "epoch",
      "use_lora": true
    }
  }'
```

The response will include a `fine_tune` object. For example:

```json theme={null}
{
  "fine_tune": {
    "id": "ft_12345",
    "status": "created",
    "resource": "main/train_financial_sentiment.parquet",
    "base_model": "Qwen/Qwen3-0.6B",
    "script_type": "text_generation",
    "training_params": { ... }
  }
}
```

Save the `id` (for example `ft_12345`) for the next steps.

If you have `jq` installed, you can capture it directly:

```bash theme={null}
FT_ID=$(curl --silent --location "${OXEN_BASE_URL:-https://hub.oxen.ai}/api/repos/${OXEN_NAMESPACE:-Tutorials}/${OXEN_REPO:-FinancialSentiment}/fine_tunes" \
  -H "Authorization: Bearer ${OXEN_API_KEY}" \
  -H "Content-Type: application/json" \
  --data '{
    "resource": "main/train_financial_sentiment.parquet",
    "base_model": "Qwen/Qwen3-0.6B",
    "script_type": "text_generation",
    "training_params": {
      "question_column": "text",
      "answer_column": "sentiment",
      "epochs": 1,
      "batch_size": 1,
      "learning_rate": 0.0001,
      "grad_accum": 1,
      "lora_alpha": 16,
      "lora_rank": 16,
      "seq_length": 1024,
      "logging_steps": 10,
      "enable_thinking": false,
      "neftune_noise_alpha": 0.0,
      "save_steps_ratio": 0.25,
      "save_strategy": "epoch",
      "use_lora": true
    }
  }' | jq -r '.fine_tune.id')

echo "Created fine-tune: $FT_ID"
```

***

## Step 2 – Start the Fine-Tune Run

Once you have a `fine_tune.id`, trigger the run.

**Endpoint**

* `POST /api/repos/{owner}/{repo}/fine_tunes/{fine_tune_id}/actions/run`

**Example `curl` request**:

```bash theme={null}
curl --location "${OXEN_BASE_URL:-https://hub.oxen.ai}/api/repos/${OXEN_NAMESPACE:-Tutorials}/${OXEN_REPO:-FinancialSentiment}/fine_tunes/${FT_ID}/actions/run" \
  -H "Authorization: Bearer ${OXEN_API_KEY}" \
  -X POST
```

This mirrors the Python example:

```python theme={null}
run_url = f"{BASE_URL}/api/repos/{NAMESPACE}/{REPO}/fine_tunes/{fine_tune_id}/actions/run"
requests.post(run_url, headers=headers)
```

***

## Step 3 – Monitor Fine-Tune Status

You can poll the fine-tune to see when it completes.

**Endpoint**

* `GET /api/repos/{owner}/{repo}/fine_tunes/{fine_tune_id}`

**Example `curl` loop** (bash):

```bash theme={null}
while true; do
  RESP=$(curl --silent "${OXEN_BASE_URL:-https://hub.oxen.ai}/api/repos/${OXEN_NAMESPACE:-Tutorials}/${OXEN_REPO:-FinancialSentiment}/fine_tunes/${FT_ID}" \
    -H "Authorization: Bearer ${OXEN_API_KEY}")

  echo "$RESP" | jq '.'

  STATUS=$(echo "$RESP" | jq -r '.fine_tune.status')
  echo "Status: $STATUS"

  if [ "$STATUS" = "completed" ]; then
    OUTPUT_RESOURCE=$(echo "$RESP" | jq -r '.fine_tune.output_resource')
    echo "Fine-tune completed! Output: $OUTPUT_RESOURCE"
    break
  elif [ "$STATUS" = "errored" ]; then
    ERROR_MSG=$(echo "$RESP" | jq -r '.fine_tune.error')
    echo "Fine-tune failed: $ERROR_MSG"
    exit 1
  elif [ "$STATUS" = "stopped" ]; then
    echo "Fine-tune was stopped"
    break
  fi

  # Wait 30 seconds before checking again (matches Python example)
  sleep 30
done
```

This shell loop is the `curl` equivalent of the Python monitoring loop:

```python theme={null}
status_url = f"{BASE_URL}/api/repos/{NAMESPACE}/{REPO}/fine_tunes/{fine_tune_id}"
while True:
    response = requests.get(status_url, headers=headers)
    fine_tune = response.json()["fine_tune"]
    status = fine_tune["status"]
    ...
    time.sleep(30)
```

***

With these three steps, you have a complete end-to-end fine-tuning run using only `curl`, matching the minimal Python example but fully scriptable from the command line.