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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.

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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"

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Prerequisites

  • Repository on Oxen with your training data committed, for example:
    • Namespace: Tutorials
    • Repository: FinancialSentiment
  • Dataset resource inside that repo, for example:
  • 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: 
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

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Step 1 – Create a Fine-Tune

Endpoint
  • POST /api/repos/{owner}/{repo}/fine_tunes
Example curl request (mirrors the Python payload): 
curl --location "${OXEN_BASE_URL:-https://hub.oxen.ai}/api/repos/${OXEN_NAMESPACE:-Tutorials}/${OXEN_REPO:-FinancialSentiment}/fine_tunes" \
  -H "Authorization: Bearer ${OXEN_API_KEY}" \
  -H "Content-Type: application/json" \
  --data '{
    "resource": "main/train_financial_sentiment.parquet",
    "base_model": "Qwen/Qwen3-0.6B",
    "script_type": "text_generation",
    "training_params": {
      "question_column": "text",
      "answer_column": "sentiment",
      "epochs": 1,
      "batch_size": 1,
      "learning_rate": 0.0001,
      "grad_accum": 1,
      "lora_alpha": 16,
      "lora_rank": 16,
      "seq_length": 1024,
      "logging_steps": 10,
      "enable_thinking": false,
      "neftune_noise_alpha": 0.0,
      "save_steps_ratio": 0.25,
      "save_strategy": "epoch",
      "use_lora": true
    }
  }'
The response will include a fine_tune object. For example: 
{
  "fine_tune": {
    "id": "ft_12345",
    "status": "created",
    "resource": "main/train_financial_sentiment.parquet",
    "base_model": "Qwen/Qwen3-0.6B",
    "script_type": "text_generation",
    "training_params": { ... }
  }
}
Save the id (for example ft_12345) for the next steps. If you have jq installed, you can capture it directly: 
FT_ID=$(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"

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

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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): 
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: 
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.