| Feature | Description | API Highlights | Benefits | |---|---|---|---| | | Supports nested prompts ( parent → child → leaf ) with inheritance of slots and formatting. | PromptSpec.load(yaml_path) , PromptNode.add_child() | Reuse common scaffolding (e.g., “You are a helpful assistant”) across tasks. | | Dynamic Curriculum Learning | Two modes: offline (pre‑computed difficulty) and online (model‑in‑the‑loop). | CurriculumScheduler(mode="online") | Faster convergence, mitigates catastrophic forgetting. | | Plug‑in Data Providers | Uniform DataProvider base class for custom generators (LLM‑based, rule‑based, crowd‑sourced). | @register_provider(name="self_instruct") | Extensible to any synthetic data pipeline. | | Multimodal Slot Types | Slots can now hold Image , Audio , Video , or Tensor objects. | slot_type: image , image_path: "img_url" | Enables few‑shot vision‑language, audio‑language tasks. | | Versioned Prompt Repositories | Built‑in Git‑compatible storage for prompt specifications. | PromptRepo.clone(url, rev="v2.1") | Reproducibility across experiments. | | Telemetry & Logging | Structured JSON logs of prompt rendering, difficulty scores, and model latency. | Telemetry.enable(level="info") | Easier debugging and audit. | | CLI & Web UI | fewfeed CLI for quick prototyping; optional Flask‑based UI for visual curriculum inspection. | fewfeed run --config cfg.yaml | Lowers entry barrier for non‑programmers. |
FewFeed v2 is the second major release of the , a lightweight, extensible system for generating, curating, and serving high‑quality training examples to modern large‑language models (LLMs) and multimodal models in low‑resource regimes. This paper provides a self‑contained, practical reference for researchers and engineers who wish to adopt FewFeed v2 in their pipelines. We describe the design goals, architecture, and new capabilities introduced in v2 (e.g., hierarchical prompting, dynamic curriculum learning, and plug‑in data sources). We then detail a step‑by‑step usage workflow, best‑practice recommendations, and an empirical evaluation on three benchmark tasks (text classification, code generation, and multimodal captioning). Finally, we discuss current limitations and outline avenues for future development.
| Category | Representative Systems | Core Idea | Relation to FewFeed | |---|---|---|---| | Prompt‑Engineering Toolkits | PromptSource (2023), OpenAI Prompt‑Library (2024) | Centralized repositories of hand‑crafted prompts | FewFeed provides a that feeds prompts, not just stores them. | | Curriculum‑Learning Frameworks | Curriculum Learning Toolkit (CLT, 2022), AutoCurriculum (2024) | Adaptive sampling of training data based on difficulty | FewFeed v2 embeds a plug‑in curriculum module that can be swapped with any external scheduler. | | Data‑Augmentation for Few‑Shot | T0‑Adapt (2023), Self‑Instruct (2024) | Automatic generation of synthetic examples | FewFeed’s SyntheticProvider API abstracts these generators, making them interchangeable. | | Multimodal Few‑Shot Systems | Flamingo (2022), GIT (2023) | Joint text‑image prompting | FewFeed v2 adds MultimodalSlot support for images, audio, and video embeddings. | v2 fewfeed
As of early 2026, the ecosystem around FewFeed V2 has seen significant shifts: How Create Link on Sp0m Facebook - TikTok
project/ │ ├─ data/ │ └─ imdb_reviews.jsonl # "text": "...", "label": "pos/neg" │ ├─ prompts/ │ └─ sentiment.yaml # hierarchical prompt spec │ ├─ config/ │ └─ run.yaml # master configuration │ └─ main.py # driver script | Feature | Description | API Highlights |
name: SentimentAnalysis description: Few‑shot sentiment classification for movie reviews.
# Rendering template for a single example example_template: | Review: "review" Sentiment: "label" | | Multimodal Slot Types | Slots can
10 April 2026
SocialBu : Offers all-in-one scheduling and automation for multiple networks.
FewFeed V2 offers a suite of specialized tools tailored for rapid social media growth: