---
base_model: Qwen/Qwen2.5-1.5B-Instruct
library_name: peft
license: mit
language:
  - en
pipeline_tag: text-generation
tags:
  - promptforge
  - prompt-optimization
  - prompt-engineering
  - lora
  - peft
  - qwen2.5
  - text-generation
  - base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct
  - transformers
---

# 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](https://github.com/arjun988/promptModel) — 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`](https://huggingface.co/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`](https://huggingface.co/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:

| Weak | Optimized (intent preserved) |
|------|------------------------------|
| `Make an app about social media like facebook and stuff` | Social media / Facebook-like app prompt with profiles, feed, likes, constraints, output format |

### 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`](https://pypi.org/project/tuneprompt/) (recommended)

```bash
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"
```

```python
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`](https://pypi.org/project/tuneprompt/1.0.0/) on PyPI · **Import:** `promptforge` · **CLI:** `tuneprompt` / `promptforge` · **Code:** https://github.com/arjun988/promptModel

### Load the adapter directly (PEFT)

```python
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

| Setting | Value |
|---------|-------|
| Base model | `Qwen/Qwen2.5-1.5B-Instruct` |
| Method | LoRA (PEFT) |
| LoRA rank / alpha | 16 / 32 |
| Target modules | q/k/v/o + MLP projections |
| Max sequence length | 512 |
| Epochs | 6 |
| Effective batch size | 8 (batch 1 × grad accum 8) |
| Learning rate | 1e-4 |
| Precision | fp16 |
| Gradient checkpointing | enabled |
| Config | `configs/optimizer_fast_8gb.yaml` |

#### 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

| Signal | Result |
|--------|--------|
| Validation loss | **0.121** |
| Example quality lift (scorer) | e.g. **41.5 → 94.0** on a social-media app prompt |
| Intent preservation | Topic keywords retained (social / Facebook) |
| Validation gate | Rejects empty / repetitive / low-intent outputs |

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

```bibtex
@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.