Fireworks launches fine-tuning service - Rapidly iterate on quality and scale to production through Fireworks inference

Tl;dr

• Fine-tuning service launch Fireworks is offering an affordable LoRA fine-tuning service for 10 models, including Mixtral. The service costs $2 per 1M training examples for Mixtral and similarly competitive rates for other models
• Tune and iterate rapidly - Our ultra-fast tuning and model deployment process lets you go from dataset to querying a fine-tuned model in minutes. Quickly switch between and compare models. Fireworks lets you keep 100 models and immediately ready for usage, with no extra cost.
• Seamless, cost-free integration into blazing-fast inference - Your fine-tuned models are served serverless on the Fireworks inference platform with no additional costs and with fast speeds like our base models

Intro

Fireworks is excited to announce our fine-tuning service to better enable customized models to be used with our blazing fast inference service! Fine-tuning is a crucial part of improving model accuracy on individual use cases. In the past year, we’ve seen huge usage of our “model deployment” service that allows users to upload models to Fireworks that were fine-tuned on other services. As part of Fireworks’ commitment to building the best production platform, we’re excited to offer fine-tuning directly on the Fireworks platform. Our service is focused on rapidly iterating on and seamlessly deploying fine-tuned models.

Context - What is fine-tuning?

Fine-tuning is the process of providing many examples to an LLM to improve model performance. Specifically, Fireworks’ fine-tuning process uses a technique called LoRA that enables improved performance without requiring large amounts of data or reductions in model speed. Fine-tuning is helpful with a variety of tasks, especially those that require customization for specific formats or domains, like generating SQL code or responding with a specific tone.

Fine-tuning Challenges

Fine-tuning can offer huge increases in model accuracy and provide a “moat” for your app through differentiated quality. However, the process of fine-tuning can be painful, due to:

• Iteration Time - Existing platforms require hours to fine-tune a model, additional time to deploy the model and still more time to boot up GPUs for serving
• Fine-tuning Cost A single fine-tuning job can cost dozens of dollars between the fine-tuning process and GPU loading. This lengthy and costly fine-tuning process may need to be repeated dozens of times when iterating
• Slow and Expensive Model Serving - Even after you’ve created the perfect model, existing platforms charge 2x the cost to serve fine-tuned models or require models to be served on slower or more expensive dedicated GPU capacity

Why fine-tune on Fireworks?

Fine-tuning on Fireworks offers

• Seamless, cost-free integration into blazing fast inference platform Fireworks hosts fine-tuned models using the same set-up as our “serverless” base models, so fine-tuned models are fast, with speeds up to 300 tokens/sec. (Note that fine-tuned model speeds are slightly slower than base model speeds due to technical constraints). We also don’t charge additional cost to use a fine-tuned model. You’ll pay $0.50 / 1M tokens for inference on fine-tuned Mixtral, exactly like you would for base Mixtral. Therefore, running inference on fine-tuned models on Fireworks should offer inference speeds several times that of competitors, at half the cost or lower (especially our fastest models of Mixtral and Llama 70b chat)
• Immediate iteration and experimentation Fireworks tuned models can be deployed in a ~minute and used instantly without waiting for GPUs to be booted up. Fireworks lets you deploy up to 100 fine-tuned models, so you can quickly compare models and swap them into live services. There’s no cost to deploying fine-tuned models, you’ll only pay for traffic used.

• Competitive cost to tune - Unlike other platforms, we don’t charge additional service fees for fine-tuning. We charge only per token of training data, as shown below (dataset size * number of epochs), with a minimum charge of $3. Both our rates for training and for minimum charges are lower than competitors. Competing platforms charge $4 / 1M training tokens for Mixtral and large models and $1/1M training tokens for smaller models

How to tune

Tuning a model is easy on Fireworks through our “Firectl” command line interface (see docs).  Let’s say that you wanted to tune Mixtral-8x-7b-instruct to have a specific tone/accuracy for instruction following by using the databricks/databricks-dolly-15k dataset. First, you just prepare a dataset in a JSONL file - we even provide code (see documentation) to convert any dataset to a JSONL file. The data would appear like so:

{"instruction": "Which is a species of fish? Tope or Rope", "context": "", "response": "Tope", "category": "classification"}
 
{"instruction": "Why can camels survive for long without water?", "context": "", "response": "Camels use the fat in their humps to keep them filled with energy and hydration for long periods of time.", "category": "open_qa"}

Next, you provide fine-tuning settings to specify which model to use, how the model should format/interpret the training data and hyperparameter settings. We enable 2 hyperparameters to be changed - epochs and learning rate. You can also connect your fine-tuning job to Weights and Biases to view progress visually in their interface.

# The ID of the dataset you created above.
dataset: my-dataset
 
text_completion:
  # How the fields of the JSON dataset should be formatted into the input text.
  input_template: "### GIVEN THE CONTEXT: {context}  ### INSTRUCTION: {instruction}  ### RESPONSE IS: "
  
  # How the fields of the JSON dataset should be formatted into the output text.
  output_template: "ANSWER: {response}"
 
# The Hugging Face model name of the base model.
base_model: mistralai/Mistral-7B-v0.1

Afterwards, you simply provide your data and your settings in the Firectl to start a fine-tuning job and will be provided with a model to deploy and use seamlessly with the Fireworks inference service.

Conclusion

Fine-tuning is a powerful tool that enables you to quickly provide improved accuracy to your users. Fireworks makes it faster and easier to tune and serve models. Get started today with our fine-tuning docs (link). Tune, deploy and compare fine-tuned models within minutes with Fireworks’ near-instantaneous deployment of fine-tuned models. When you’ve found a model you like, serve it fast and cost-effectively at scale through the Fireworks inference engine.

We’ll be investing further in our tuning service in the coming months, with planned features like special fine-tuning options for conversational formats and function calling.  We’d love to hear your feedback! Please join the #fine-tuning channel of our Discord to chat directly with the team or other fine-tuning users. We’re excited to see what you build!