import streamlit as st from defaults import ARGILLA_URL from hub import push_pipeline_params from utils import project_sidebar st.set_page_config( page_title="Domain Data Grower", page_icon="🧑‍🌾", ) project_sidebar() ################################################################################ # HEADER ################################################################################ st.header("🧑‍🌾 Domain Data Grower") st.divider() st.subheader("Step 3. Run the pipeline to generate synthetic data") st.write("Define the distilabel pipeline for generating the dataset.") hub_username = st.session_state.get("hub_username") project_name = st.session_state.get("project_name") hub_token = st.session_state.get("hub_token") ############################################################### # CONFIGURATION ############################################################### st.divider() st.markdown("## 🧰 Data Generation Pipeline") st.markdown( """ Now we need to define the configuration for the pipeline that will generate the synthetic data. The pipeline will generate synthetic data by combining self-instruction and domain expert responses. The self-instruction step generates instructions based on seed terms, and the domain expert step generates \ responses to those instructions. Take a look at the [distilabel docs](https://distilabel.argilla.io/latest/sections/learn/tasks/text_generation/#self-instruct) for more information. """ ) ############################################################### # INFERENCE ############################################################### st.markdown("#### 🤖 Inference configuration") st.write( """Add the url of the Huggingface inference API or endpoint that your pipeline should use to generate instruction and response pairs. \ Some domain tasks may be challenging for smaller models, so you may need to iterate over your task definition and model selection. \ This is a part of the process of generating high-quality synthetic data, human feedback is key to this process. \ You can find compatible models here:""" ) with st.expander("🤗 Recommended Models"): st.write("All inference endpoint compatible models can be found via the link below") st.link_button( "🤗 Inference compaptible models on the hub", "https://huggingface.co/models?pipeline_tag=text-generation&other=endpoints_compatible&sort=trending", ) st.write("🔋Projects with sufficient resources could take advantage of LLama3 70b") st.code( "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct" ) st.write("🪫Projects with less resources could take advantage of LLama 3 8b") st.code( "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct" ) st.write("🍃Projects with even less resources could use Phi-3-mini-4k-instruct") st.code( "https://api-inference.huggingface.co/models/microsoft/Phi-3-mini-4k-instruct" ) st.write("Note Hugggingface Pro gives access to more compute resources") st.link_button( "🤗 Huggingface Pro", "https://huggingface.co/pricing", ) self_instruct_base_url = st.text_input( label="Model base URL for instruction generation", value="https://api-inference.huggingface.co/models/microsoft/Phi-3-mini-4k-instruct", ) domain_expert_base_url = st.text_input( label="Model base URL for domain expert response", value="https://api-inference.huggingface.co/models/microsoft/Phi-3-mini-4k-instruct", ) ############################################################### # PARAMETERS ############################################################### st.divider() st.markdown("#### 🧮 Parameters configuration") st.write( "⚠️ Model and parameter choices significantly affect the quality of the generated data. \ We reccomend that you start with generating a few samples and review the data. Then scale up from there. \ You can run the pipeline multiple times with different configurations and append it to the same Argilla dataset." ) st.markdown( "Number of generations are the samples that each model will generate for each seed term, \ so if you have 10 seed terms, 2 instruction generations, and 2 response generations, you will have 40 samples in total." ) self_intruct_num_generations = st.slider( "Number of generations for self-instruction", 1, 10, 2 ) domain_expert_num_generations = st.slider( "Number of generations for domain expert response", 1, 10, 2 ) st.markdown( "Temperature is a hyperparameter that controls the randomness of the generated text. \ Lower temperatures will generate more deterministic text, while higher temperatures \ will add more variation to generations." ) self_instruct_temperature = st.slider("Temperature for self-instruction", 0.1, 1.0, 0.9) domain_expert_temperature = st.slider("Temperature for domain expert", 0.1, 1.0, 0.9) ############################################################### # ARGILLA API ############################################################### st.divider() st.markdown("#### 🔬 Argilla API details to push the generated dataset") st.markdown( "Here you can define the Argilla API details to push the generated dataset to your Argilla space. \ These are the defaults that were set up for the project. You can change them if needed." ) argilla_url = st.text_input("Argilla API URL", ARGILLA_URL) argilla_api_key = st.text_input("Argilla API Key", "owner.apikey") argilla_dataset_name = st.text_input("Argilla Dataset Name", project_name) st.divider() ############################################################### # Pipeline Run ############################################################### st.markdown("## Run the pipeline") st.markdown( "Once you've defined the pipeline configuration above, you can run the pipeline from your local machine." ) if all( [ argilla_api_key, argilla_url, self_instruct_base_url, domain_expert_base_url, self_intruct_num_generations, domain_expert_num_generations, self_instruct_temperature, domain_expert_temperature, hub_username, project_name, hub_token, argilla_dataset_name, ] ) and st.button("💾 Save Pipeline Config"): with st.spinner("Pushing pipeline to the Hub..."): push_pipeline_params( pipeline_params={ "argilla_api_key": argilla_api_key, "argilla_api_url": argilla_url, "argilla_dataset_name": argilla_dataset_name, "self_instruct_base_url": self_instruct_base_url, "domain_expert_base_url": domain_expert_base_url, "self_instruct_temperature": self_instruct_temperature, "domain_expert_temperature": domain_expert_temperature, "self_intruct_num_generations": self_intruct_num_generations, "domain_expert_num_generations": domain_expert_num_generations, }, hub_username=hub_username, hub_token=hub_token, project_name=project_name, ) st.success( f"Pipeline configuration pushed to the dataset repo {hub_username}/{project_name} on the Hub." ) st.markdown( "To run the pipeline locally, you need to have the `distilabel` library installed. \ You can install it using the following command:" ) st.code( body=""" # Install the distilabel library pip install distilabel """, language="bash", ) st.markdown("Next, you'll need to clone the pipeline code and install dependencies:") st.code( """ git clone https://github.com/huggingface/data-is-better-together cd data-is-better-together/domain-specific-datasets/distilabel_pipelines pip install -r requirements.txt huggingface-cli login """, language="bash", ) st.markdown("Finally, you can run the pipeline using the following command:") st.code( f""" python domain_expert_pipeline.py {hub_username}/{project_name}""", language="bash", ) st.markdown( "👩‍🚀 If you want to customise the pipeline take a look in `domain_expert_pipeline.py` \ and the [distilabel docs](https://distilabel.argilla.io/)" ) st.markdown( "🚀 Once you've run the pipeline your records will be available in the Argilla space" ) st.link_button("🔗 Argilla Space", argilla_url) st.markdown("Once you've reviewed the data, you can publish it on the next page:") st.page_link( page="pages/4_🔍 Review Generated Data.py", label="Review Generated Data", icon="🔍", ) else: st.info("Please fill all the required fields.")