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language:
- en
license: apache-2.0
base_model: unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- nodejs
- javascript
- backend
- express
- gguf
- ollama
pipeline_tag: text-generation
---
# 🚀 nodejs-coder-qwen25
A fine-tuned **Qwen2.5-Coder-7B-Instruct** model specialized for **Node.js backend development**, trained with LoRA adapters using Unsloth, merged into a single GGUF file for efficient local inference with Ollama.
---
## 🧠 Model Description
| Property | Details |
|----------|---------|
| **Base Model** | `Qwen2.5-Coder-7B-Instruct` |
| **Fine-tuning Method** | LoRA (Low-Rank Adaptation) via Unsloth |
| **Training Framework** | TRL SFTTrainer |
| **Quantization** | GGUF Q4_K_M (~4.4 GB) |
| **Context Length** | 2048 tokens |
| **Language** | JavaScript / Node.js |
This model is specifically trained to write clean, production-ready Node.js backend code. It understands common backend patterns including REST APIs, database integrations, authentication, and testing.
---
## 🎯 Specialties
- ✅ **Express.js** — REST APIs, middleware, routing
- ✅ **NestJS** — modules, controllers, services, guards
- ✅ **Sequelize / Prisma** — ORM models, migrations, queries
- ✅ **MongoDB / Mongoose** — schemas, models, aggregations
- ✅ **PostgreSQL / pg** — raw queries, connection pooling
- ✅ **JWT Authentication** — login, token generation, guards
- ✅ **Jest** — unit tests, mocking, integration tests
- ✅ **Async/Await** — file I/O, error handling, promises
---
## ⚡ Quick Start with Ollama
### Step 1 — Download files
```bash
hf download Sriram-214/nodejs-coder-qwen25 nodejs-coder-Q4_K_M.gguf --local-dir ./
hf download Sriram-214/nodejs-coder-qwen25 Modelfile --local-dir ./
```
### Step 2 — Create Ollama model
```bash
ollama create nodejs-coder -f Modelfile
```
### Step 3 — Run
```bash
ollama run nodejs-coder
```
---
## 📋 Modelfile
```dockerfile
FROM ./nodejs-coder-Q4_K_M.gguf
PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER repeat_penalty 1.1
PARAMETER num_ctx 2048
PARAMETER num_gpu 0
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|im_start|>"
TEMPLATE """<|im_start|>system
{{ .System }}<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"""
SYSTEM """You are a Node.js backend development expert. You write clean, efficient, production-ready code following best practices. You specialize in Express.js, NestJS, Sequelize, Prisma, MongoDB, PostgreSQL, and Jest testing. Always include error handling and follow RESTful API design principles."""
```
> **Note:** Set `num_gpu 0` for CPU-only mode on GPUs with ≤6GB VRAM. For GPUs with more VRAM, increase this value.
---
## 💬 Example Prompts & Outputs
### Express REST API
**Prompt:** `Write a simple Express.js REST API with a GET /health endpoint`
```javascript
const express = require('express');
const app = express();
app.get('/health', (req, res) => {
res.status(200).json({ status: 'ok' });
});
app.listen(3000, () => console.log('Server running on port 3000'));
```
### Async File Reading
**Prompt:** `Write a Node.js function that reads a JSON file using async/await`
```javascript
const fs = require('fs').promises;
async function readJsonFile(filePath) {
try {
const data = await fs.readFile(filePath, 'utf-8');
return JSON.parse(data);
} catch (error) {
throw new Error(`Failed to read file: ${error.message}`);
}
}
```
### List Directory Files
**Prompt:** `Write a simple Node.js code to list all the files in a directory`
```javascript
const fs = require('fs');
fs.readdir('./', (err, files) => {
if (err) throw err;
console.log(files);
});
```
---
## 🏋️ Training Details
| Parameter | Value |
|-----------|-------|
| **Base Model** | `unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit` |
| **LoRA Rank** | 16 |
| **LoRA Alpha** | 32 |
| **Training Data** | Node.js backend code dataset |
| **Training Framework** | Unsloth + TRL SFTTrainer |
| **Training Environment** | Google Colab (T4 GPU) |
| **Quantization** | Q4_K_M via llama.cpp |
---
## ⚠️ Limitations
- Optimized for Node.js/JavaScript — not suited for other languages
- Context window of 2048 tokens — long files may be truncated
- CPU inference is slow (~3-5 tokens/sec on modern CPUs)
- May occasionally produce outdated library syntax
---
## 📄 License
Apache 2.0 — see [LICENSE](https://www.apache.org/licenses/LICENSE-2.0)
|