Build your own LLM data engine.
Automate
and simplify your workflow with ease
Build scalable synthetic data pipelines in minutes.
Research-backed by leading AI labs
Use cases
Augment and evaluate your data with one framework.
Use cases
Augment and evaluate your data with one framework.
Use cases
Augment and evaluate your data with one framework.
Code supervised fine-tuning (SFT) data generation
Generate data for any code language with the methods used to train the Llama, Nemotron, and DeepSeek Coder LLMs.
Code supervised fine-tuning (SFT) data generation
Generate data for any code language with the methods used to train the Llama, Nemotron, and DeepSeek Coder LLMs.
You are an expert Verilog programmer. Come up with a module that solves the following question {input} with these constraints: {constraints}.
Code extraction and validation:
pyverilog
Generate outputs
You are an expert Verilog programmer. Come up with a module that solves the following question {input} with these constraints: {constraints}.
AI pipelines are essential for building robust, scalable, and efficient AI systems.
Generate outputs
Code supervised fine-tuning (SFT) data generation
Generate data for any code language with the methods used to train the Llama, Nemotron, and DeepSeek Coder LLMs.
You are an expert Verilog programmer. Come up with a module that solves the following question {input} with these constraints: {constraints}.
AI pipelines are essential for building robust, scalable, and efficient AI systems.
Generate outputs
How it works
Build a custom pipeline in <100 lines.
How it works
Build a custom pipeline in <100 lines.
How it works
Build a custom pipeline in <100 lines.
Define generation prompts
Put in prompt templates for input generation, output generation, and judge LLMs.
Define generation prompts
Put in prompt templates for input generation, output generation, and judge LLMs.
Define validation and curation functions
Bring your own validation logic or use our built-in validation and curation methods.
Define validation and curation functions
Bring your own validation logic or use our built-in validation and curation methods.
Hit pipeline.run()
and wait for your data to generate.
Hit pipeline.run()
and wait for your data to generate.
# Initialize pipeline pipeline = Pipeline( instructions_path=instructions_file, api_key=api_key, output_model="Llama-3.3-70B-Instruct", judge_model="Llama-3.3-70B-Instruct", language="Rust", output_prompt=rust_generation_prompt, judge_prompt=rust_judge_prompt, temperature=0.7, samples=1, syntax_check=False, deduplicate=True, custom_validation_fn=RustValidation().check, # Pass Rust syntax and compilation check custom_extractor=extract_rust_code # Extract Rust code from response ) # Run pipeline results = pipeline.run()
Features
The best synthetic data practices - streamlined.
Generate, validate, and curate your most performant data in one pipeline.
Features
The best synthetic data practices - streamlined.
Features
The best synthetic data practices - streamlined.
Generate, validate, and curate your most performant data in one pipeline.
Diverse data generation
Use a combination of evolutionary and self-instruct methods
Diverse data generation
Use a combination of evolutionary and self-instruct methods
Diverse data generation
Use a combination of evolutionary and self-instruct methods
Built-in verification
Ensure only the highest-quality and relevant data samples make it into your dataset.
Built-in verification
Ensure only the highest-quality and relevant data samples make it into your dataset.
Built-in verification
Ensure only the highest-quality and relevant data samples make it into your dataset.
Data selection
Auto-curate your data by only selecting the data samples with the most training signal
Data selection
Auto-curate your data by only selecting the data samples with the most training signal
Data selection
Auto-curate your data by only selecting the data samples with the most training signal
Be your own data vendor.
Generate and evaluate any dataset on-demand.