---
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-Instruct
tags:
- peft
- lora
- fastapi
- rag
- guardrails
- structured-outputs
language:
- en
pipeline_tag: text-generation
library_name: peft
---

# 🧠 Qwen2.5-1.5B FastAPI Guardrails & RAG LoRA Adapter

A specialized Parameter-Efficient Fine-Tuned (PEFT / LoRA) adapter for **`Qwen/Qwen2.5-1.5B-Instruct`** tailored for FastAPI multi-tenant schema isolation, circuit breaker implementation, and grounded RAG citation alignment.

Developed by **Harmehak Singh Khangura** ([Hugging Face Profile](https://huggingface.co/harmehak0173)).

---

## 📌 Model Description

This model adapter enhances `Qwen2.5-1.5B-Instruct` with domain-specific knowledge in:
* **FastAPI Backend Engineering**: Dependency injection for multi-tenant schema isolation and JWT auth contracts.
* **Resiliency & Circuit Breakers**: Degradation patterns and circuit breaker states for external LLM APIs.
* **Grounded RAG Systems**: Citation alignment, quality gate similarity scoring, and hallucination reduction.

---

## 🚀 How to Use

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

BASE_MODEL = "Qwen/Qwen2.5-1.5B-Instruct"
LORA_ADAPTER = "harmehak0173/qwen2.5-1.5b-fastapi-guardrails-lora"

tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)
base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Load LoRA Adapter
model = PeftModel.from_pretrained(base_model, LORA_ADAPTER)

prompt = "<|im_start|>user\nHow do you implement a Circuit Breaker for external LLM APIs in Python?<|im_end|>\n<|im_start|>assistant\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🛠️ Training Details
Base Model: Qwen/Qwen2.5-1.5B-Instruct
Fine-Tuning Method: LoRA (Rank r=16, lora_alpha=32)
Target Modules: q_proj, k_proj, v_proj, o_proj
Trainer: Hugging Face TRL SFTTrainer
Format: ChatML (<|im_start|> / <|im_end|>)