Creating a Training Job

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Starting a Training Experiment

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Model Details

• Base Model: Select a supported model from the list below.
• Source Type: Currently supports models from Hugging Face.
• Model Type: Auto-filled based on the selected base model.

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Supported Models

• meta-llama/Llama-3.1-8B-Instruct
• meta-llama/Llama-3.2-1B-Instruct
• meta-llama/Llama-3.2-3B-Instruct
• meta-llama/Llama-3.2-11B-Vision-Instruct
• Qwen/Qwen2.5-3B-Instruct
• Qwen/Qwen2.5-14B-Instruct
• Qwen/Qwen2.5-VL-7B-Instruct
• tiiuae/falcon-7b-instruct

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Dataset Selection

• Source Options: Select the source of your dataset. Supported options include
  • Hugging Face (public Hub)
  • AWS S3
  • GCP Storage (GCS)
• Dataset Name
  This should be unique within your organization to help with organizing and reusing datasets.
• Dataset Path
  Specify the dataset location. For AWS S3 & GCP GCS, use the full path in the format.
  e.g., s3://your-bucket/your-file.jsonl
• Dataset Description (Optional)
  Provide a brief description of the dataset’s contents or purpose. Optional but useful for reference.
• Secret (Required for AWS S3 or GCP GCS)
  Provide your cloud credentials to enable secure access to private storage buckets.
• Region (Required for AWS S3 or GCP GCS)
  Select the region where your storage bucket is located.

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Dataset Format

• For VLM models, use a ZIP file containing both the image files and a train.jsonl file (the master training file).
  The directory should be archived in a .zip file and stored in an object storage.
  Example zip command:cd path/to/dataset_dir && zip -r dataset_dir.zip ./*

1. ShareGPT Format
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   Ask AI

   {
     "system": "<system>",
     "conversation": [
       {"human": "<query1>", "assistant": "<response1>"},
       {"human": "<query2>", "assistant": "<response2>"}
     ]
   }
   

2. OpenAI SFT Format
   Copy

   Ask AI

   {
     "messages": [
       {"role": "system", "content": "<system>"},
       {"role": "user", "content": "<query1>"},
       {"role": "assistant", "content": "<response1>"},
       {"role": "user", "content": "<query2>"},
       {"role": "assistant", "content": "<response2>"}
     ]
   }
   

3. OpenAI DPO Format (for preference training)
   Copy

   Ask AI

   {
     "messages": [
       {"role": "system", "content": "You are a useful and harmless assistant"},
       {"role": "user", "content": "Tell me tomorrow's weather"},
       {"role": "assistant", "content": "Tomorrow's weather will be sunny"}
     ],
     "rejected_response": "I don't know"
   }    
   

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Dataset Configuration

• Lazy Tokenize: Delay tokenization until needed. Speeds up dataset loading for large files.
• Streaming: Enable only for public HF Datasets to load records on-the-fly, reducing local storage needs.
• Prompt Max Length: Maximum token length for prompt. Longer sequences will be truncated.

  Recommended: 2048

• System Prompt: (Optional) A global prefix to every example, e.g., You are a helpful assistant.
• Prompt Template: (Optional) If your data needs wrapping in a custom template, e.g., <system> {system_prompt} <user> {prompt}.
• Train/Validation Split: Percentage (fraction) for splitting your .jsonl into training and validation sets.
  • Split Type
    Currently, only random split is supported. The dataset will be randomly divided into training and validation sets.
  • Train Split Ratio
    Enter the ratio of data to be used for training (e.g., 0.9 for 90%).
  • Validation Split Ratio
    Enter the ratio of data to be used for validation (e.g., 0.1 for 10%).
    Train Split Ratio should be greater than 0.8

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Infrastructure Configuration

• GPU Type: Select instance GPU, e.g., H100, L40s.
• GPU Count: Number of GPUs to allocate for this job.

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Training Configuration

1. Core Options

2. RLHF Configuration (Applicable only for RLHF Training type)
   When selecting Training Type = RLHF, additional configuration fields appear under RLHF Config. These vary depending on the chosen RLHF Type. The platform supports the following RLHF variants:
   • DPO (Direct Preference Optimization)
     • Beta
       Controls the trade-off between preference loss and KL regularization.
       Default: 0.3
       Optional: Yes, but recommended.
   • GRPO (Generative Rollouts with Preference Optimization)
     • Beta
       Similar to DPO, this governs the preference vs. KL loss balance.
       Default: 0.0001
     • Max Num Seqs
       Number of sequences to use during rollout.
       Default: 1
       Recommended Value: 1
     • Enforce Eager
       If enabled, forces rollouts to run in eager mode rather than compiled mode. Useful for debugging or compatibility issues.
       Default: Unchecked
       Recommended: We suggest enabling Enforce Eager during GRPO training.
   • Common Parameters:

     Field	Description	Required	Default
     RLHF Type	Select the RLHF variant to use	✅	-
     Reference Model	Path to the baseline model used for KL regularization	Optional	-
     Reward Model	Path to the reward mode	Optional	-

3. Optimization Hyperparameters

   Parameter	Description	Default Values	Recommended Values	Permissible Range
   Num Epochs	Number of full passes through the dataset	1	2-5	< 50
   Train Batch Size	Samples per device for training	8	8	< 16
   Eval Batch Size	Samples per device for evaluation	1	8	<16
   Learning Rate	Initial learning rate for optimizer	0.0001	1×10⁻⁵ to 2×10⁻⁵	< 5×10⁻⁵
   Dataloader Num Workers	Parallel data-loading threads per device	1	4	<10

3. Checkpointing & Monitoring

   Parameter	Description	Default	Recommended Values	Permissible Range
   Save Steps	Interval (in steps) between saving model checkpoints.	100	100	<= 100
   Save Total Limit	Max number of checkpoints to keep locally.	2	2-5	<10
   Eval Steps	Interval (in steps) between running evaluation loop.	100	100	100 - 200
   Logging Steps	Interval (in steps) between logging metrics to the dashboard.	5	5	< 20

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LoRA Adapter Configuration

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Distributed Training Configuration

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Launching Your Job

1. Review all settings.
2. Click Create Job.
3. Monitor progress under My Trainings > Your Training Job > Metrics .
4. Compile the model and deploy when training completes.