---
license: apache-2.0
base_model: unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- lora
- fastapi
- code-assistant
language:
- en
library_name: transformers
pipeline_tag: text-generation
---

# Qwen2.5-Coder-7B-FastAPI-LoRA

A LoRA fine-tune of **Qwen2.5-Coder-7B-Instruct** specialized as a **FastAPI documentation assistant**. The model is trained to answer questions, generate code, and explain concepts related to the FastAPI framework, covering everything from basic routing to advanced topics like security and testing.

## Model Details

- **Developed by:** [LadiesMan69](https://huggingface.co/LadiesMan69)
- **License:** apache-2.0
- **Base model:** [unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit)
- **Model type:** Causal decoder-only language model (Qwen2 architecture)
- **Fine-tuning method:** LoRA (Low-Rank Adaptation)
- **Language:** English
- **Trained with:** [Unsloth](https://github.com/unslothai/unsloth) + Hugging Face TRL — 2x faster training

## Motivation

General-purpose code models are often imprecise or outdated when it comes to framework-specific APIs. This model was fine-tuned on a curated dataset of FastAPI-focused instruction/response pairs to produce a lightweight, deployable assistant that gives accurate, idiomatic answers for building and debugging FastAPI applications.

## Training Data

The fine-tuning dataset was built specifically for this task using the **ChatML** format and organized into topic categories, including:

- **Tutorial** — core concepts: path/query parameters, request bodies, response models, dependency injection
- **Advanced** — background tasks, middleware, WebSockets, custom exception handlers, lifespan events
- **Security** — OAuth2/JWT authentication, password hashing, CORS, rate limiting
- **Testing** — `TestClient` usage, pytest fixtures, mocking dependencies, async test patterns

Examples were generated in batches per category to ensure balanced topic coverage and consistent formatting across the dataset.

## Intended Use

- Answering questions about FastAPI concepts, patterns, and best practices
- Generating FastAPI route handlers, Pydantic models, and dependency-injected services
- Explaining and debugging FastAPI-related code snippets
- Acting as an in-editor or chat-based documentation assistant for developers working with FastAPI

## How to Use

### With `transformers`

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "LadiesMan69/Qwen2.5-Coder-7B-FastAPI-LoRA"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

messages = [
    {"role": "system", "content": "You are a helpful FastAPI documentation assistant."},
    {"role": "user", "content": "How do I add JWT-based authentication to a FastAPI route?"},
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
```

### With `unsloth`

```python
from unsloth import FastModel

model, tokenizer = FastModel.from_pretrained(
    model_name="LadiesMan69/Qwen2.5-Coder-7B-FastAPI-LoRA",
    max_seq_length=2048,
)
```

### With `vLLM`

```bash
pip install vllm
vllm serve "LadiesMan69/Qwen2.5-Coder-7B-FastAPI-LoRA"
```

```bash
curl -X POST "http://localhost:8000/v1/chat/completions" \
  -H "Content-Type: application/json" \
  --data '{
    "model": "LadiesMan69/Qwen2.5-Coder-7B-FastAPI-LoRA",
    "messages": [
      {"role": "user", "content": "Show me a minimal FastAPI app with a health check endpoint."}
    ]
  }'
```

## Prompt Format

This model uses the ChatML-style chat template built into the tokenizer (`apply_chat_template`). For best results, include a system message establishing the assistant's role as a FastAPI expert, followed by the user's question.

## Limitations

- Focused specifically on FastAPI; general coding ability outside this domain is inherited from the base model and not specifically enhanced.
- As with any LLM, generated code should be reviewed and tested before use in production.
- May not reflect the very latest FastAPI releases if they postdate the training data.

## Training Procedure

Fine-tuned using LoRA adapters on top of the 4-bit quantized `unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit` base model, leveraging Unsloth's optimized training kernels for faster, memory-efficient fine-tuning.

## Model Tree

- Base: [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B)
- → [Qwen/Qwen2.5-Coder-7B](https://huggingface.co/Qwen/Qwen2.5-Coder-7B)
- → [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct)
- → [unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit](https://huggingface.co/unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit) (quantized)
- → **LadiesMan69/Qwen2.5-Coder-7B-FastAPI-LoRA** (this model, LoRA fine-tune)

## Acknowledgements

This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's TRL library.

[![Made with Unsloth](https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png)](https://github.com/unslothai/unsloth)