PromptForge-Optimizer

LoRA adapter that rewrites weak / vague prompts into clear, specific, actionable LLM prompts while preserving the original intent and topic.

Part of PromptForge — local-first prompt quality scoring + optimization.

Model Details

Model Description

PromptForge-Optimizer is a PEFT/LoRA fine-tune of Qwen/Qwen2.5-1.5B-Instruct. Given a weak user prompt (plus optional quality analysis context), it generates an improved prompt with audience, constraints, structure, and output format — without changing the core topic.

• Developed by: PromptForge contributors
• Model type: Causal LM adapter (LoRA / PEFT)
• Language(s): English
• License: MIT
• Finetuned from: Qwen/Qwen2.5-1.5B-Instruct

Model Sources

• Repository: https://github.com/arjun988/promptModel
• Companion model: PromptForge-Quality (ModernBERT multi-dimension prompt scorer)
• Demo: Gradio app in the PromptForge repo (demo/app.py)

Uses

Direct Use

• Rewrite vague prompts into production-ready LLM instructions
• Pair with PromptForge-Quality for score → optimize → re-score workflows
• Local / offline prompt tooling (CLI, Python API, Gradio)

Example weak → strong:

Downstream Use

• Prompt engineering assistants
• IDE / agent tooling that improves user instructions before calling an LLM
• Synthetic data pipelines that need higher-quality prompts

Out-of-Scope Use

• Not a general chat assistant
• Not a substitute for domain experts (legal, medical, safety-critical advice)
• Not guaranteed to preserve intent on topics far outside the curated training set
• Do not use to generate harmful, deceptive, or disallowed content

Bias, Risks, and Limitations

• Trained on curated synthetic weak→strong pairs; coverage is strongest on coding apps, writing, data, research, and planning prompts
• May invent plausible audience / stack details (e.g. “product managers”, “Flask”) when the weak prompt is underspecified
• Small base model (1.5B) — quality is good for local use, not frontier-LLM rewrite quality
• Inference includes validation + fallback in the PromptForge package; raw adapter output alone may still drift

Recommendations

• Prefer the PromptForge Python package / CLI (chat template + stop tokens + validation) over raw generate
• For new domains, add your own weak→strong pairs and retrain the LoRA
• Always review optimized prompts before sending them to production LLMs

How to Get Started with the Model

Install & use with tuneprompt (recommended)

pip install tuneprompt

python -m promptforge download \
  --quality-repo ArjunShukla/PromptForge-Quality \
  --optimizer-repo ArjunShukla/PromptForge-Optimizer

python -m promptforge run "Make an app about social media like facebook and stuff"
# or: tuneprompt run "Make an app about social media like facebook and stuff"

from promptforge import PromptForge

pf = PromptForge(
    quality_model_path="ArjunShukla/PromptForge-Quality",
    optimizer_model_path="ArjunShukla/PromptForge-Optimizer",
)
print(pf.run("Build me a website for a startup")["optimized_prompt"])

Package: tuneprompt on PyPI · Import: promptforge · CLI: tuneprompt / promptforge · Code: https://github.com/arjun988/promptModel

Load the adapter directly (PEFT)

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "Qwen/Qwen2.5-1.5B-Instruct"
adapter = "ArjunShukla/PromptForge-Optimizer"

tokenizer = AutoTokenizer.from_pretrained(adapter, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(base, trust_remote_code=True, device_map="auto")
model = PeftModel.from_pretrained(model, adapter)

Use Qwen’s chat template (tokenizer.apply_chat_template) — do not hand-roll <|system|> tags.

Training Details

Training Data

• ~800 curated high-quality weak → strong prompt pairs
• ~140 unique topic-preserving seeds (coding, writing, data, research, general)
• Intent rule: optimized prompt must keep the same topic as the weak prompt
• Assistant-only loss masking (system/user tokens not trained)

Training Procedure

Training Hyperparameters

Speeds, Sizes, Times

• Hardware: NVIDIA GeForce RTX 5060 Laptop GPU (8 GB)
• Wall time: ~87 minutes (6 epochs)
• Adapter size on disk: ~82 MB
• Train loss: ~0.47
• Validation loss: ~0.121

Evaluation

Metrics

Evaluation is primarily: held-out SFT loss + pipeline checks (score delta, instruction preservation, repetition detection). Not a public leaderboard benchmark.

Summary

The adapter reliably expands vague prompts into structured instructions on in-distribution topics. Off-distribution prompts may fall back to a safer template when used through PromptForge.

Environmental Impact

• Hardware Type: NVIDIA RTX 5060 Laptop (8 GB)
• Hours used: ~1.5 h for this adapter run
• Cloud Provider: N/A (local)
• Compute Region: N/A
• Carbon Emitted: Not measured

Technical Specifications

Model Architecture and Objective

• Causal language model (Qwen2.5 Instruct) + LoRA
• Objective: SFT to map weak prompt (+ analysis) → optimized prompt text only

Compute Infrastructure

Hardware

• RTX 5060 Laptop GPU, 8 GB VRAM

Software

• PyTorch (CUDA)
• Transformers
• PEFT / LoRA
• PromptForge training scripts

Framework versions

• PEFT 0.20.0

Citation

@software{promptforge_optimizer,
  title = {PromptForge-Optimizer},
  author = {PromptForge Contributors},
  year = {2026},
  url = {https://huggingface.co/ArjunShukla/PromptForge-Optimizer}
}

Model Card Contact

Open an issue on the PromptForge GitHub repository.