---
license: apache-2.0
language:
- en
base_model:
- likithyadavv/codementor-7b
pipeline_tag: text-generation
library_name: transformers
tags:
- code
- code-generation
- code-explanation
- bug-detection
- lora
- peft
- 4bit
- qlora
- fullstack
- python
- javascript
- fastapi
- codementor
metrics:
- accuracy
---

# 🤖 CodeMentor V2 — Fullstack AI Code Assistant

> **Code Smarter. Debug Faster. Learn Better.**

CodeMentor V2 is a LoRA fine-tuned large language model specialized in **fullstack code explanation, bug detection, and improvement suggestions**. Built on top of CodeLlama-7B-Instruct, it is optimized for real-time developer assistance via a REST API.

---

## 📋 Model Details

| Property | Value |
|---|---|
| **Model Type** | Causal Language Model (LoRA Adapter) |
| **Base Model** | `codellama/CodeLlama-7b-Instruct-hf` |
| **Fine-Tuning Method** | QLoRA (4-bit quantization + LoRA) |
| **LoRA Rank** | 16 |
| **Training Framework** | HuggingFace PEFT + TRL |
| **Language** | English |
| **License** | Apache 2.0 |
| **Adapter Size** | ~162 MB |

---

## 🎯 Intended Use

CodeMentor V2 is designed for:

- **Code Explanation** — Understand what a block of code does in plain English
- **Bug Detection** — Identify logic errors, missing base cases, off-by-ones, etc.
- **Code Improvement** — Suggest better patterns, optimizations, and best practices
- **Fullstack Q&A** — Answer programming questions across Python, JavaScript, and more
- **Developer Mentorship** — Act as an always-available senior developer

---

## 🚀 Quick Start

### Load with PEFT (Recommended)

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

# 4-bit quantization config
bnb = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16
)

BASE_MODEL = "codellama/CodeLlama-7b-Instruct-hf"
ADAPTER    = "likithyadavv/codementor-v2-fullstack"

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    quantization_config=bnb,
    device_map="auto"
)

# Load LoRA adapter
model     = PeftModel.from_pretrained(base_model, ADAPTER)
tokenizer = AutoTokenizer.from_pretrained(ADAPTER)

print("✅ CodeMentor loaded!")
```

### Run Inference

```python
def ask_codementor(instruction, code_input="", max_new_tokens=512):
    prompt = f"### Instruction:\n{instruction}\n\n### Input:\n{code_input}\n\n### Response:\n"
    
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            temperature=0.2,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id,
        )
    response = tokenizer.decode(
        outputs[0][inputs["input_ids"].shape[1]:],
        skip_special_tokens=True
    )
    return response.strip()

# Example usage
print(ask_codementor(
    instruction="Explain this code and identify any bugs.",
    code_input="def factorial(n): return n * factorial(n-1)"
))
```

**Output:**
```
This is a recursive factorial function. However, it has a critical bug —
there is no base case, so it will recurse infinitely and raise a
RecursionError. Fix:

def factorial(n):
    if n == 0:      # ← base case added
        return 1
    return n * factorial(n - 1)
```

---

## 💬 Interactive Chat Loop

```python
chat_history = []

while True:
    user_input = input("\n👤 You: ").strip()
    if user_input.lower() in ["exit", "quit"]:
        break

    # Build context from last 3 exchanges
    context = ""
    for u, b in chat_history[-3:]:
        context += f"User: {u}\nAssistant: {b}\n\n"

    is_code = any(x in user_input for x in ["def ", "class ", "import ", "return ", "=>"])
    instruction = (
        "Explain this code, identify any bugs, and suggest improvements."
        if is_code else
        "Answer this programming question clearly and concisely."
    )

    full_input = f"{context}User: {user_input}" if context else user_input
    response = ask_codementor(instruction, full_input)

    print(f"\n🤖 CodeMentor: {response}")
    chat_history.append((user_input, response))
```

---

## 🌐 Deploy as REST API (FastAPI + ngrok)

```python
from fastapi import FastAPI
from pydantic import BaseModel
import uvicorn, nest_asyncio, threading
from pyngrok import ngrok

app = FastAPI(title="CodeMentor API")

class AskRequest(BaseModel):
    instruction: str
    input: str = ""

@app.get("/")
def root():
    return {"status": "CodeMentor API is live 🚀"}

@app.get("/health")
def health():
    return {"status": "ok"}

@app.post("/ask")
def ask(req: AskRequest):
    response = ask_codementor(req.instruction, req.input)
    return {"response": response}

# Launch
nest_asyncio.apply()
public_url = ngrok.connect(8000)
print(f"🚀 Live at: {public_url}/docs")

threading.Thread(
    target=lambda: uvicorn.run(app, host="0.0.0.0", port=8000, log_level="warning"),
    daemon=True
).start()
```

**Example curl:**
```bash
curl -X POST https://YOUR-NGROK-URL/ask \
  -H "Content-Type: application/json" \
  -d '{"instruction": "Explain and fix this code", "input": "def f(n): return n*f(n-1)"}'
```

---

## 📊 Evaluation

| Metric | Score |
|---|---|
| Code Explanation Accuracy | **92.6%** |
| Bug Detection Rate | **89.3%** |
| Improvement Suggestion Quality | **4.1 / 5.0** |
| Avg. Response Latency (T4 GPU) | **~3.2s** |

> Evaluated on a held-out set of 500 fullstack coding tasks across Python, JavaScript, and SQL.

---

## 🗂️ Training Details

```
Dataset:        Custom fullstack coding instruction dataset
                (code explanations, bug fixes, Q&A pairs)
Format:         Alpaca-style (### Instruction / ### Input / ### Response)
Base Model:     codellama/CodeLlama-7b-Instruct-hf
Method:         QLoRA — 4-bit NF4 quantization + LoRA adapters
LoRA Config:    r=16, alpha=32, dropout=0.05
                target_modules: q_proj, v_proj, k_proj, o_proj
Epochs:         3
Batch Size:     4 (gradient accumulation: 4)
Learning Rate:  2e-4 with cosine scheduler
Hardware:       Google Colab A100 (40GB)
Training Time:  ~4 hours
```

---

## ⚙️ Hardware Requirements

| Setup | Minimum | Recommended |
|---|---|---|
| GPU VRAM | 8 GB (4-bit) | 16 GB+ |
| RAM | 12 GB | 24 GB |
| GPU | T4 | A100 / RTX 3090+ |
| Storage | 15 GB | 20 GB |

> ✅ Runs on **free Google Colab T4** with 4-bit quantization.

---

## ⚠️ Limitations

- Responses may occasionally hallucinate for very niche or obscure APIs
- Best results on Python and JavaScript; other languages have lower coverage
- Long code blocks (>200 lines) may exceed context window — chunk inputs
- Not suitable for security-critical code auditing without human review

---

## 📚 Citation

```bibtex
@misc{codementor-v2-fullstack,
  author       = {Likith Yadav},
  title        = {CodeMentor V2: A LoRA Fine-Tuned Fullstack Code Assistant},
  year         = {2025},
  publisher    = {HuggingFace},
  howpublished = {\url{https://huggingface.co/likithyadavv/codementor-v2-fullstack}},
}
```

---

## 🔗 Links

- 🤗 **Model Repo:** [likithyadavv/codementor-v2-fullstack](https://huggingface.co/likithyadavv/codementor-v2-fullstack)
- 📖 **Base Model:** [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf)
- 🏫 **Institution:** MVJ College of Engineering, Bengaluru, India