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