metadata
license: mit
base_model: microsoft/Phi-3.5-mini-instruct
tags:
- phi3
- prompt-engineering
- syntaxa
- fine-tuned
- instruction-tuning
model_creator: saleen
model_type: phi3
language:
- en
pipeline_tag: text-generation
Syntaxa-Prompt-Gen (Phi-3.5-mini-Instruct Fine-Tuned)
Syntaxa is a specialized fine-tuned version of Microsoft's Phi-3.5-mini-instruct. It is designed to act as a "Prompt Generator," turning simple persona descriptions into detailed, high-quality system prompts for other LLMs.
🚀 Model Details
- Developed by: Saleh (saleen)
- Model type: Causal Language Model (Transformer-based)
- Base Model: microsoft/Phi-3.5-mini-instruct
- Finetuning Technique: LoRA (Low-Rank Adaptation)
- Training Focus: Instruction following for Persona-based prompt generation.
🎯 Intended Use
Syntaxa is intended to help users bridge the gap between a simple idea and a professional prompt.
- Input Format:
### Instruction: Act as a [Persona]. Write a prompt for yourself.\n\n### Response: - Output: A comprehensive, structured system prompt including variables and specific constraints.
🛠️ Training Procedure
The model was fine-tuned using the following configuration:
- Epochs: 3
- Batch Size: 2 (with Gradient Accumulation Steps: 4)
- Learning Rate: 2e-4
- Scheduler: Cosine
- Precision: FP16
- Dataset: Custom instruction-set focusing on the "Awesome ChatGPT Prompts" structure.
💻 How to Use
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "saleen/Syntaxa_Final_Full"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=False)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
prompt = "### Instruction: Act as a Senior Web Developer. Write a prompt for yourself.\n\n### Response:"
print(pipe(prompt, max_new_tokens=256)[0]['generated_text'])