README.md

---
license: apache-2.0
base_model:
- Qwen/Qwen2.5-Coder-1.5B-Instruct
tags:
- gguf
- ollama
- code
- text-generation
- reactjs
- nodejs
model_type: qwen2
pipeline_tag: text-generation
quantized_by: llama-quantize
---

# Videxpulse-Coder-1.5B-GGUF

This is a fine-tuned version of Qwen 1.5B, quantized to Q4_K_M GGUF format. It is optimized specifically for full-stack web development.

## Specialization
- ReactJS, NodeJS, ExpressJS
- MySQL, PostgreSQL
- WordPress, PHP

## How to use with Ollama

### Quick Start

1. Download the `videxpulse-coder-1.5b-q4_k_m.gguf` and the `Modelfile`.
2. Run the following command in your terminal to create the model:
   ```bash
   ollama create Videxpulse-Coder -f ./Modelfile
   ```
3. Run the model:
   ```bash
   ollama run Videxpulse-Coder
   ```

### Creating a Modelfile

If you need to create or customize the Modelfile, follow these instructions:

1. **Create a new file named `Modelfile`** (no extension) in the directory containing your GGUF model file.

2. **Add the base model reference:**
   ```
   FROM ./videxpulse-coder-1.5b-q4_k_m.gguf
   ```
   Replace `videxpulse-coder-1.5b-q4_k_m.gguf` with your actual GGUF file name if different.

3. **Configure model parameters** (optional but recommended):
   ```
   PARAMETER temperature 0.2
   PARAMETER stop "<|im_start|>"
   PARAMETER stop "<|im_end|>"
   ```
   - `temperature`: Controls randomness in responses (0.0-1.0). Lower values (0.2) produce more deterministic code.
   - `stop`: Defines tokens where the model should stop generating.

4. **Define the chat template** (required for proper formatting):
   ```
   TEMPLATE """{{ if .System }}<|im_start|>system
   {{ .System }}<|im_end|>
   {{ end }}{{ if .Prompt }}<|im_start|>user
   {{ .Prompt }}<|im_end|>
   {{ end }}<|im_start|>assistant
   {{ .Response }}<|im_end|>"""
   ```
   This template ensures proper message formatting for the Qwen model architecture.

5. **Set the system prompt** (customize based on your needs):
   ```
   SYSTEM """You are Videxpulse-Coder, an advanced open-source AI assistant fine-tuned specifically to write elite ReactJS, NodeJS, expressJS, MYSQL, Postgresql, WordPress, PHP code. You are an independent AI assistant optimized for software engineering."""
   ```

6. **Save the Modelfile** and verify it's in the same directory as your GGUF file.

7. **Create the Ollama model** using the Modelfile:
   ```bash
   ollama create Videxpulse-Coder -f ./Modelfile
   ```

### Complete Modelfile Example

Here's a complete example of a ready-to-use Modelfile:

```
FROM ./videxpulse-coder-1.5b-q4_k_m.gguf

PARAMETER temperature 0.2
PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>"

TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ .Response }}<|im_end|>"""

SYSTEM """You are Videxpulse-Coder, an advanced open-source AI assistant fine-tuned specifically to write elite ReactJS, NodeJS, expressJS, MYSQL, Postgresql, WordPress, PHP code. You are an independent AI assistant optimized for software engineering."""
```
## Hardware & Estimation Requirements

This model is heavily optimized for resource-constrained local environments. Below are the resource estimations for efficient execution:

### 📊 RAM & VRAM Footprint
* **Model File Size:** ~1.02 GB
* **Minimum VRAM Required:** ~1.40 GB (For full GPU offloading with 2k context window)
* **Minimum System RAM:** 4 GB (If running purely on CPU/System memory)

### 💻 Supported Hardware Classes
* **Apple Silicon:** M1, M2, M3, M4 series (Runs entirely in unified memory via Ollama)
* **NVIDIA GPUs:** GTX 10-series, RTX 20/30/40/50 series (Requires >2GB VRAM for full offload)
* **CPU Architectures:** x86_64 with AVX2 instruction set support