File size: 2,880 Bytes
809871a 5ab3e8a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | ---
license: apache-2.0
language:
- en
pipeline_tag: text-classification
tags:
- coding
- code
- fyp
- free
- opensource
- powerful
- lite
- cheap
- agent
---
# 🚀 omriX
**A well-trained small coding agent that is very quick.**
`omriX` is a lightweight, open-source coding-focused model designed for speed, efficiency, and practical developer workflows. With a compact disk size of roughly **3 GB**, it’s ideal for local inference, low-cost deployments, and experimentation without heavy hardware requirements.
---
## ✨ Key Features
- ⚡ **Fast inference** — optimized for quick responses
- 🧠 **Coding-focused** — tuned for programming and code-related tasks
- 📦 **Lightweight** — ~3 GB disk size
- 🔓 **Open source** — Apache-2.0 license
- 💸 **Cheap to run** — suitable for low-resource environments
- 🤖 **Agent-friendly** — works well as a small coding agent component
---
## 🧩 Use Cases
- Code understanding & classification
- Lightweight coding assistants
- Student projects / FYPs
- Local developer tools
- Agent pipelines requiring fast, small models
- Prototyping and experimentation
---
## 📥 Model Details
- **Model ID:** `xlelords/omriX`
- **License:** Apache-2.0
- **Language:** English
- **Pipeline Tag:** Text Classification
- **Disk Size:** ~3 GB
- **Status:** Fully open-source
---
## 🚀 Quick Start
Example usage with 🤗 Transformers:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
model_id = "xlelords/omriX"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
text = "Explain what this function does: def add(a, b): return a + b"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
print(outputs.logits)
```
> ⚠️ Note: The pipeline interface depends on your downstream setup.
> `omriX` is commonly used as a **component model inside agents** or custom pipelines.
---
## 🛠️ Agent Integration
`omriX` is well-suited for use in lightweight agent frameworks such as:
- Custom Python agents
- Tool-calling pipelines
- smolagents-style workflows
- Local or edge deployments
Its small size and fast responses make it ideal for chaining with tools or running alongside other models.
---
## 📄 License
This model is released under the **Apache-2.0 License**.
You are free to use, modify, distribute, and build upon it — even commercially.
---
## 🤝 Contributing
Contributions are welcome!
Feel free to open issues or pull requests for:
- Documentation improvements
- Benchmarks
- Agent examples
- Optimized inference setups
---
## ⭐ Final Notes
If you’re looking for a **fast, cheap, and capable small coding agent**, `omriX` is built to get out of your way and let you ship.
Enjoy 🚀
|