---
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- code
- coding-assistant
- arc
- arc-brains
- hackathon
- ppt
- qwen2
- lora
- sft
language:
- en
pipeline_tag: text-generation
library_name: transformers
---

# 🌟 Arc — Friendly Coding Expert

> **Created by Arc Brains: Ibrahim Shaikh, Harsh Goswami, Manas Tamore, Ayush Thakur**

Arc is a powerful coding assistant fine-tuned from **Qwen2.5-Coder-7B-Instruct** on **253K+ high-quality coding examples**. It delivers **complete, production-ready solutions** — never patches.

---

## 🎯 What Arc Excels At

| Skill | Description |
|-------|-------------|
| 💻 **Complete Code** | Full runnable solutions with imports, error handling, docs |
| 🏗️ **Hackathon Projects** | Entire apps from scratch — Flask, React, CLI tools |
| 📊 **Presentations** | Generate PowerPoint slides programmatically |
| 🐛 **Debugging** | Root-cause analysis with full fixes, not band-aids |
| 🌐 **Multi-language** | Python, JavaScript, C++, Java, Ruby, Go, Rust, and more |
| 📐 **Architecture** | Full project structure with all files |

---

## 🚀 Quick Start

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

# Load
base = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-Coder-7B-Instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
model = PeftModel.from_pretrained(base, "ibrahim2806/Arc")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct")

# Chat
messages = [
    {"role": "system", "content": "You are Arc, a friendly coding expert by Arc Brains."},
    {"role": "user", "content": "Build a full Flask REST API for a todo app with CRUD, auth, and SQLite"}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=4096, temperature=0.7, do_sample=True)
print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```

---

## 📊 Training Details

| Parameter | Value |
|-----------|-------|
| **Base Model** | Qwen/Qwen2.5-Coder-7B-Instruct |
| **Method** | QLoRA (4-bit NF4) |
| **LoRA Rank** | 64 |
| **LoRA Alpha** | 128 |
| **Target Modules** | q/k/v/o_proj, gate/up/down_proj |
| **Learning Rate** | 2e-4 (cosine decay) |
| **Epochs** | 3 |
| **Effective Batch Size** | 32 |
| **Max Sequence Length** | 4096 |
| **Total Training Samples** | ~253,000 |
| **Optimizer** | AdamW |
| **Precision** | BF16 |

---

## 📚 Training Datasets

| Dataset | Samples | What It Teaches |
|---------|---------|-----------------|
| [m-a-p/Code-Feedback](https://huggingface.co/datasets/m-a-p/Code-Feedback) | 68K | Multi-turn debugging, project building, iterative refinement |
| [Magicoder-Evol-Instruct-110K](https://huggingface.co/datasets/ise-uiuc/Magicoder-Evol-Instruct-110K) | 110K | Complex algorithmic & system design problems |
| [Magicoder-OSS-Instruct-75K](https://huggingface.co/datasets/ise-uiuc/Magicoder-OSS-Instruct-75K) | 75K | Real-world code from open-source projects |

---

## 🏗️ Arc Brains Team

Built with ❤️ by **Ibrahim Shaikh**, **Harsh Goswami**, **Manas Tamore**, and **Ayush Thakur**.