Fine-Tune: Text Chat Messages

Default fine-tune schema

Overview

This schema is used for fine-tuning models with text chat messages 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_chat_messages",
  "training_params": {
    ...
  }
}

Key Parameters:

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

Supported Models

• OpenAI/GPT-OSS-20B (openai/gpt-oss-20b)
• Llama 3.1 8B Instruct (meta-llama/Llama-3.1-8B-Instruct)
• Llama 4 Scout (meta-llama/Llama-4-Scout-17B-16E-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
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)
messages_column	string	Yes	Messages (input) Column (DataFrame column name)
neftune_noise_alpha	number	No	(default: 0)
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

Request Body

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

Field Details

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

messages_column

Messages (input) Column

Type: string

neftune_noise_alpha

Type: number

Default: 0

Minimum: 0

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