Finetuning with Together AI — The Easiest SFT Tutorial

This streamlined tutorial guides you through the finetuning process with Together AI. While the official tutorial splits the process across different pages, this guide consolidates everything into a single, easy-to-follow resource.

Note:

All commands should be entered in the terminal.
The minimal training cost is $5, even with just one entry in the training data.

1. Authentication

Start by setting your Together AI API key:

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export TOGETHER_API_KEY= <your_api>

2. Prepare Your Dataset

Construct your dataset according to the required data format. You can use either Conversational Data or Instruction Data formats.

Conversational Data Example

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{
  "messages": [
    {"role": "system", "content": "This is a system prompt."},
    {"role": "user", "content": "Hello, how are you?"},
    {"role": "assistant", "content": "I'm doing well, thank you! How can I help you?"},
    {"role": "user", "content": "Can you explain machine learning?"},
    {"role": "assistant", "content": "Machine learning is..."}
  ]
}

Instruction Data Example

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{"prompt": "...", "completion": "..."}
{"prompt": "...", "completion": "..."}

3. Upload Your Dataset and Obtain File ID

Upload your dataset using the following command:

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together files upload <file_name>

Replace <file_name> with the name of your dataset file (e.g., dataset.jsonl).

Upon successful upload, you will receive a response similar to:

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{
    "id": "file-123456",
    "object": "file",
    "created_at": 1734574470,
    "purpose": "fine-tune",
    "filename": "filename.jsonl",
    "bytes": 0,
    "line_count": 0,
    "processed": false,
    "FileType": "jsonl"
}

Action: Note down the id (e.g., file-123456) for use in the next steps.

4. Select a Model to Fine-Tune

Fine-tuning ModelsA list of all the models available for fine-tuning.docs.together.ai

Use the name listed under the "Model String for API" column. For example: "meta-llama/Llama-3.3--70B-Instruct-Reference"

5. Create a Finetuning Task

Initiate the finetuning process with the following command:

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together fine-tuning create - training-file file-123456 - model meta-llama/Llama-3.3–70B-Instruct-Reference

Replace:

file-123456 with your actual file ID.
meta-llama/Llama-3.3--70B-Instruct-Reference with your chosen model string.

If the submission is successful, you will see a response similar to:

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Submitting a fine-tuning job with the following parameters:
FinetuneRequest(
    training_file='file-123456',
    validation_file='',
    model='meta-llama/Llama-3.3–70B-Instruct-Reference',
    n_epochs=1,
    learning_rate=1e-05,
    lr_scheduler=FinetuneLRScheduler(lr_scheduler_type='linear', lr_scheduler_args=FinetuneLinearLRSchedulerArgs(min_lr_ratio=0.0)),
    warmup_ratio=0.0,
    max_grad_norm=1.0,
    weight_decay=0.0,
    n_checkpoints=1,
    n_evals=0,
    batch_size=32,
    suffix=None,
    wandb_key=None,
    wandb_base_url=None,
    wandb_project_name=None,
    wandb_name=None,
    training_type=LoRATrainingType(type='Lora', lora_r=8, lora_alpha=16, lora_dropout=0.0, lora_trainable_modules='all-linear'),
    train_on_inputs='auto'
)
Successfully submitted a fine-tuning job ft-c1cce2b0-1a90-47e4-8e84-46f76d2c3dcb at 12/19/2024, 10:16:38

Action: Note down the fine-tuning job ID (e.g., ft-c1cce2b0-1a90-47e4-8e84-46f76d2c3dcb).

6. Monitor and Use Your Fine-Tuned Model

Once the finetuning job is complete, you can use your fine-tuned model as follows:

Example in Python

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from together import Together

client = Together()

response = client.chat.completions.create(
    model="check your model name in your together AI dashboard",
    messages=[{"role": "user", "content": "Could you give me a like?"}],
)
print(response.choices[0].message.content)