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

python

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'])

saleen/Syntaxa_Final_full

Syntaxa-Prompt-Gen (Phi-3.5-mini-Instruct Fine-Tuned)

🚀 Model Details

🎯 Intended Use

🛠️ Training Procedure

💻 How to Use

saleen/
Syntaxa_Final_full