personal-prompt-engineer-qwen1.5b API & Inference Endpoint

Personal Prompt Engineer (Qwen2.5-1.5B LoRA)

Personal Prompt Engineer is a QLoRA fine-tuned LoRA adapter built on Qwen2.5-1.5B-Instruct. It specializes in transforming short, vague, or incomplete prompts into structured, detailed, and execution-ready prompts while preserving the user's original intent.

Unlike a general chatbot, this model is designed to rewrite prompts, not answer them.

Model Details

Table with columns: Property, Value
Property	Value
Base Model	Qwen2.5-1.5B-Instruct
Fine-tuning Method	QLoRA
Adapter Type	LoRA
Quantization	4-bit NF4
Framework	Transformers + PEFT
Primary Task	Prompt Rewriting

Intended Use

The model is designed for:

Prompt Engineering
AI Assistants
RAG Pipelines
Workflow Automation
Coding Assistants
Content Generation
Agentic AI Systems

Typical workflow:

markdown
User Prompt
      ↓
Personal Prompt Engineer
      ↓
Optimized Prompt
      ↓
GPT / Claude / Gemini / Llama / Qwen

Example

Input

markdown
make a react app with dark mode

Output

markdown
Create a modern React application with a responsive user interface that includes a dark/light mode toggle.

Requirements:
- Use React functional components.
- Implement state management using Context API.
- Persist theme preference in localStorage.
- Include accessible UI components.
- Follow clean project architecture.
- Provide complete source code with explanations.

Training Dataset

The adapter was fine-tuned on approximately 3,030 curated prompt rewriting examples spanning:

Software Development
Business
Marketing
Creative Writing
Education
Science
Productivity
General AI Prompting

Each example consists of:

Instruction
Draft Prompt
Rewritten Prompt

Training Configuration

Table with columns: Hyperparameter, Value
Hyperparameter	Value
LoRA Rank	16
LoRA Alpha	32
LoRA Dropout	0
Optimizer	paged_adamw_8bit
Learning Rate	2e-4
Scheduler	Cosine
Epochs	2
Sequence Length	512
Batch Size	4

Evaluation

Table with columns: Epoch, Training Loss, Validation Loss, Mean Token Accuracy
Epoch	Training Loss	Validation Loss	Mean Token Accuracy
1	1.1990	1.1171	74.08%
2	0.9854	1.0420	76.45%

The validation loss consistently decreased during training, indicating good convergence without obvious signs of overfitting.

Installation

bash
pip install transformers peft accelerate bitsandbytes

Usage

import warnings warnings.filterwarnings("ignore")

Bypass torchao metadata version check across all environments

import importlib.metadata _orig_version = importlib.metadata.version def _mock_version(package_name, *args, **kwargs): if package_name == "torchao": return "1.0.0" return _orig_version(package_name, *args, **kwargs) importlib.metadata.version = _mock_version

import torch from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer from peft import PeftModel

1. Hugging Face Repositories

BASE_MODEL = "Qwen/Qwen2.5-1.5B-Instruct" LORA_ADAPTER = "aiml8726737/personal-prompt-engineer-qwen1.5b"

2. Device Selection

device = "cuda" if torch.cuda.is_available() else ("mps" if hasattr(torch.backends, "mps") and torch.backends.mps.is_available() else "cpu") dtype = torch.float16 if device in ["cuda", "mps"] else torch.float32

print(f"Loading Hugging Face Pipeline on {device.upper()}...")

3. Load Tokenizer & Base Model

tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) tokenizer.pad_token_id = tokenizer.eos_token_id

base_model = AutoModelForCausalLM.from_pretrained( BASE_MODEL, torch_dtype=dtype, device_map="auto" if device == "cuda" else None ) if device != "cuda": base_model = base_model.to(device)

4. Load Fine-Tuned Adapter

model = PeftModel.from_pretrained(base_model, LORA_ADAPTER)

5. Create Standard Hugging Face Text-Generation Pipeline

generator = pipeline( "text-generation", model=model, tokenizer=tokenizer, device_map="auto" if device == "cuda" else None )

SYSTEM_PROMPT = "You are an expert Personal Prompt Engineer. Your task is to rewrite vague user prompts into professional, execution-ready prompts."

6. Prompt Rewriter Function using Hugging Face Pipeline

def rewrite_prompt(draft_prompt: str) -> str: messages = [ {"role": "system", "content": SYSTEM_PROMPT}, {"role": "user", "content": f"Rewrite the following draft prompt into a professional, execution-ready prompt.\n\nDraft Prompt:\n<<>> {draft_prompt} <<>>"} ]

markdown
# Hugging Face Pipeline Execution
result = generator(
    messages,
    max_new_tokens=300,
    temperature=0.7,
    do_sample=True,
    return_full_text=False
)

return result[0]["generated_text"]

7. Test Example

if name == "main": draft = "create a landing page for an AI agent app" print("\n" + "="*70) print("DRAFT PROMPT:") print(draft) print("="*70) print("\nREWRITTEN BY HUGGINGFACE PIPELINE:") print(rewrite_prompt(draft)) print("="*70)

Limitations

Optimized for prompt rewriting rather than general question answering.
Performance depends on the quality and diversity of the training data.
May not generalize well to highly specialized domains absent from the training set.

License

This LoRA adapter is released under the Apache 2.0 License, consistent with the license of the base Qwen2.5 model.

Citation

bibtex
@misc{personalpromptengineer2026,
  title={Personal Prompt Engineer: QLoRA Fine-tuning for Prompt Rewriting},
  author={Yashvardhan Agrawal},
  year={2026},
  howpublished={Hugging Face Model Hub}
}

Personal Prompt Engineer (Qwen2.5-1.5B LoRA)

Unlike a general chatbot, this model is designed to rewrite prompts, not answer them.

Model Details

Table with columns: Property, Value
Property	Value
Base Model	Qwen2.5-1.5B-Instruct
Fine-tuning Method	QLoRA
Adapter Type	LoRA
Quantization	4-bit NF4
Framework	Transformers + PEFT
Primary Task	Prompt Rewriting

Intended Use

The model is designed for:

Prompt Engineering
AI Assistants
RAG Pipelines
Workflow Automation
Coding Assistants
Content Generation
Agentic AI Systems

Typical workflow:

markdown
User Prompt
      ↓
Personal Prompt Engineer
      ↓
Optimized Prompt
      ↓
GPT / Claude / Gemini / Llama / Qwen

Example

Input

markdown
make a react app with dark mode

Output

markdown
Create a modern React application with a responsive user interface that includes a dark/light mode toggle.

Requirements:
- Use React functional components.
- Implement state management using Context API.
- Persist theme preference in localStorage.
- Include accessible UI components.
- Follow clean project architecture.
- Provide complete source code with explanations.

Training Dataset

The adapter was fine-tuned on approximately 3,030 curated prompt rewriting examples spanning:

Software Development
Business
Marketing
Creative Writing
Education
Science
Productivity
General AI Prompting

Each example consists of:

Instruction
Draft Prompt
Rewritten Prompt

Training Configuration

Table with columns: Hyperparameter, Value
Hyperparameter	Value
LoRA Rank	16
LoRA Alpha	32
LoRA Dropout	0
Optimizer	paged_adamw_8bit
Learning Rate	2e-4
Scheduler	Cosine
Epochs	2
Sequence Length	512
Batch Size	4

Evaluation

Table with columns: Epoch, Training Loss, Validation Loss, Mean Token Accuracy
Epoch	Training Loss	Validation Loss	Mean Token Accuracy
1	1.1990	1.1171	74.08%
2	0.9854	1.0420	76.45%

The validation loss consistently decreased during training, indicating good convergence without obvious signs of overfitting.

Installation

bash
pip install transformers peft accelerate bitsandbytes

Usage

import warnings warnings.filterwarnings("ignore")

Bypass torchao metadata version check across all environments

import torch from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer from peft import PeftModel

1. Hugging Face Repositories

BASE_MODEL = "Qwen/Qwen2.5-1.5B-Instruct" LORA_ADAPTER = "aiml8726737/personal-prompt-engineer-qwen1.5b"

2. Device Selection

print(f"Loading Hugging Face Pipeline on {device.upper()}...")

3. Load Tokenizer & Base Model

tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) tokenizer.pad_token_id = tokenizer.eos_token_id

base_model = AutoModelForCausalLM.from_pretrained( BASE_MODEL, torch_dtype=dtype, device_map="auto" if device == "cuda" else None ) if device != "cuda": base_model = base_model.to(device)

4. Load Fine-Tuned Adapter

model = PeftModel.from_pretrained(base_model, LORA_ADAPTER)

5. Create Standard Hugging Face Text-Generation Pipeline

generator = pipeline( "text-generation", model=model, tokenizer=tokenizer, device_map="auto" if device == "cuda" else None )

SYSTEM_PROMPT = "You are an expert Personal Prompt Engineer. Your task is to rewrite vague user prompts into professional, execution-ready prompts."

6. Prompt Rewriter Function using Hugging Face Pipeline

markdown
# Hugging Face Pipeline Execution
result = generator(
    messages,
    max_new_tokens=300,
    temperature=0.7,
    do_sample=True,
    return_full_text=False
)

return result[0]["generated_text"]

7. Test Example

Limitations

Optimized for prompt rewriting rather than general question answering.
Performance depends on the quality and diversity of the training data.
May not generalize well to highly specialized domains absent from the training set.

License

This LoRA adapter is released under the Apache 2.0 License, consistent with the license of the base Qwen2.5 model.

Citation

bibtex
@misc{personalpromptengineer2026,
  title={Personal Prompt Engineer: QLoRA Fine-tuning for Prompt Rewriting},
  author={Yashvardhan Agrawal},
  year={2026},
  howpublished={Hugging Face Model Hub}
}

personal-prompt-engineer-qwen1.5b

README

Personal Prompt Engineer (Qwen2.5-1.5B LoRA)

Model Details

Intended Use

Example

Input

Output

Training Dataset

Training Configuration

Evaluation

Installation

Usage

Bypass torchao metadata version check across all environments

1. Hugging Face Repositories

2. Device Selection

3. Load Tokenizer & Base Model

4. Load Fine-Tuned Adapter

5. Create Standard Hugging Face Text-Generation Pipeline

6. Prompt Rewriter Function using Hugging Face Pipeline

7. Test Example

Limitations

License

Citation

Explore FriendliAI today

README

Personal Prompt Engineer (Qwen2.5-1.5B LoRA)

Model Details

Intended Use

Example

Input

Output

Training Dataset

Training Configuration

Evaluation

Installation

Usage

Bypass torchao metadata version check across all environments

1. Hugging Face Repositories

2. Device Selection

3. Load Tokenizer & Base Model

4. Load Fine-Tuned Adapter

5. Create Standard Hugging Face Text-Generation Pipeline

6. Prompt Rewriter Function using Hugging Face Pipeline

7. Test Example

Limitations

License

Citation