# neulab/prompt2model

prompt2model - Generate Deployable Models from Natural Language Instructions

Repository: https://github.com/neulab/prompt2model
Canonical: https://ross.abutalabs.com/products/prompt2model
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2024-12-29T01:55:44+00:00

## Health v2 (maintenance only)
Score: 21/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 89
- inputs: {"age_days": 1256, "days_push": 613, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2018, forks 182 (observed 2026-08-28T04:06:06.174698+00:00)

## What it is
Prompt2Model is a Python library that takes a natural language task description (like an LLM prompt) and automatically generates a small, special-purpose trained model ready for deployment. It uses LLM APIs (via LiteLLM) to generate datasets and then trains compact models, with a demo notebook and command-line interface.

## Use cases
- train a small model from a natural language task description
- distill LLM capabilities into a deployable specialized model
- generate training data automatically from a prompt
- create task-specific models without writing training code
- replace expensive LLM API calls with a small custom model
- prototype a model from a ChatGPT-style prompt

## When to choose
- you have a well-defined NLP task expressible as a prompt and want a cheap, fast specialized model
- you want to automate dataset generation and model training from instructions
- you need small models suitable for deployment instead of calling large LLM APIs

## When to avoid
- you need general-purpose LLM inference rather than narrow task models
- you have no access to LLM APIs or a GPU for training
- you need production-hardened, actively developed tooling for critical pipelines

## Facets
- artifact type: library
- maturity: maintenance
- function: llm-training, machine-learning, prompt-engineering, data-generation, sdk
- domain: machine-learning, large-language-models, artificial-intelligence
- platform: python, cli
- tags: model-distillation, llm-orchestration, huggingface, litellm, task-automation, natural-language-processing, gpu

## Member repositories
- neulab/prompt2model (main) score 21

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:06.174698+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:00:28.331117+00:00, confidence not recorded.
  - readme: https://github.com/neulab/prompt2model (fetched 2026-08-28T04:06:06.174698+00:00, sha 8e25c3dc2c8f)
- Data as of 2026-08-30T08:39:29.467469+00:00.