Fine-Tune: Text Generation

Overview

This schema is used for fine-tuning models with text generation capabilities.

Schema Type

When creating a fine-tune with this schema, use:

{
  "resource": "main/your-dataset.parquet",
  "base_model": "<model-canonical-name>",
  "script_type": "text_generation",
  "training_params": {
    ...
  }
}

Key Parameters:

script_type: text_generation (the fine-tune type)
base_model: One of the supported model canonical names below

Supported Models

Llama 3.2 1B Instruct (meta-llama/Llama-3.2-1B-Instruct)
OpenAI/GPT-OSS-20B (openai/gpt-oss-20b)
Llama 3.1 8B Instruct (meta-llama/Llama-3.1-8B-Instruct)
Llama 3.2 3B Instruct (meta-llama/Llama-3.2-3B-Instruct)
Qwen/Qwen3-1.7B (Qwen/Qwen3-1.7B)
Qwen/Qwen3-4B (Qwen/Qwen3-4B)
Qwen/Qwen3-0.6B (Qwen/Qwen3-0.6B)

Request Schema

Required Fields

Field	Type	Required	Description
`answer_column`	string	Yes	Assistant (Response) Column (DataFrame column name)
`batch_size`	integer	No	(default: 1) (min: 1)
`enable_thinking`	boolean	No	enable_thinking
`epochs`	integer	No	(default: 1) (min: 1)
`grad_accum`	integer	No	(default: 1) (min: 1)
`learning_rate`	number	No	(default: 0.0001)
`logging_steps`	integer	No	(default: 10) (min: 1)
`lora_alpha`	integer	No	(default: 16) (min: 1)
`lora_rank`	integer	No	(default: 16) (min: 1)
`neftune_noise_alpha`	number	No	(default: 0)
`question_column`	string	Yes	User (Prompt) Column (DataFrame column name)
`save_steps_ratio`	number	No	(default: 0.25)
`save_strategy`	string	No	save_strategy
`seq_length`	integer	No	(default: 1024) (min: 1)
`use_lora`	boolean	No	Use LoRA

Example Request

{
  "resource": "main/your-dataset.parquet",
  "base_model": "<model-canonical-name>",
  "script_type": "text_generation",
  "training_params": {
    "answer_column": "<answer_column>",
    "batch_size": 1,
    "enable_thinking": false,
    "epochs": 1,
    "grad_accum": 1,
    "learning_rate": 0.0001,
    "logging_steps": 10,
    "lora_alpha": 16,
    "lora_rank": 16,
    "neftune_noise_alpha": 0,
    "question_column": "<question_column>",
    "save_steps_ratio": 0.25,
    "save_strategy": "epoch",
    "seq_length": 1024,
    "use_lora": true
  }
}

import requests

url = "https://hub.oxen.ai/api/repos/{namespace}/{repo_name}/fine_tunes"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

data = {{
  "resource": "main/your-dataset.parquet",
  "base_model": "<model-canonical-name>",
  "script_type": "text_generation",
  "training_params": {{
    "answer_column": "<answer_column>",
    "batch_size": 1,
    "enable_thinking": false,
    "epochs": 1,
    "grad_accum": 1,
    "learning_rate": 0.0001,
    "logging_steps": 10,
    "lora_alpha": 16,
    "lora_rank": 16,
    "neftune_noise_alpha": 0,
    "question_column": "<question_column>",
    "save_steps_ratio": 0.25,
    "save_strategy": "epoch",
    "seq_length": 1024,
    "use_lora": true
  }}
}}

response = requests.post(url, headers=headers, json=data)
print(response.json())

curl -X POST https://hub.oxen.ai/api/repos/{namespace}/{repo_name}/fine_tunes \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{   "resource": "main/your-dataset.parquet",   "base_model": "<model-canonical-name>",   "script_type": "text_generation",   "training_params": {     "answer_column": "<answer_column>",     "batch_size": 1,     "enable_thinking": false,     "epochs": 1,     "grad_accum": 1,     "learning_rate": 0.0001,     "logging_steps": 10,     "lora_alpha": 16,     "lora_rank": 16,     "neftune_noise_alpha": 0,     "question_column": "<question_column>",     "save_steps_ratio": 0.25,     "save_strategy": "epoch",     "seq_length": 1024,     "use_lora": true   } }'

Field Details

`answer_column`

Assistant (Response) Column Type: string

`batch_size`

Type: integer Default: 1 Minimum: 1

`enable_thinking`

Type: boolean Default: false

`epochs`

Type: integer Default: 1 Minimum: 1

`grad_accum`

Type: integer Default: 1 Minimum: 1

`learning_rate`

Type: number Default: 0.0001 Minimum: 0

`logging_steps`

Type: integer Default: 10 Minimum: 1

`lora_alpha`

Type: integer Default: 16 Minimum: 1

`lora_rank`

Type: integer Default: 16 Minimum: 1

`neftune_noise_alpha`

Type: number Default: 0 Minimum: 0

`question_column`

User (Prompt) Column Type: string

`save_steps_ratio`

Type: number Default: 0.25

`save_strategy`

Type: string Default: "epoch"

`seq_length`

Type: integer Default: 1024 Minimum: 1

`use_lora`

Use LoRA Type: boolean Enable LoRA for faster fine-tuning and lower memory use Default: true

Fine-Tuning API

API Reference

fine_tunes

evaluations

Fine-Tune: Text Generation

Overview

Schema Type

Supported Models

Request Schema

Required Fields

Example Request

Field Details

`answer_column`

`batch_size`

`enable_thinking`

`epochs`

`grad_accum`

`learning_rate`

`logging_steps`

`lora_alpha`

`lora_rank`

`neftune_noise_alpha`

`question_column`

`save_steps_ratio`

`save_strategy`

`seq_length`

`use_lora`

​Overview

​Schema Type

​Supported Models

​Request Schema

​Required Fields

​Example Request

​Field Details

​answer_column

​batch_size

​enable_thinking

​epochs

​grad_accum

​learning_rate

​logging_steps

​lora_alpha

​lora_rank

​neftune_noise_alpha

​question_column

​save_steps_ratio

​save_strategy

​seq_length

​use_lora

Overview

Schema Type

Supported Models

Request Schema

Required Fields

Example Request

Field Details

`answer_column`

`batch_size`

`enable_thinking`

`epochs`

`grad_accum`

`learning_rate`

`logging_steps`

`lora_alpha`

`lora_rank`

`neftune_noise_alpha`

`question_column`

`save_steps_ratio`

`save_strategy`

`seq_length`

`use_lora`