---
base_model: unsloth/qwen2.5-coder-1.5b-instruct
tags:
- Omnionix
- Avara
- qwen2
- code
- math
- logic
- transformers
- unsloth
num_parameters: 2000000000
license: apache-2.0
language:
- en
pipeline_tag: text-generation
---
# Avara X1 Mini
Avara X1 Mini is a lightweight AI model developed by **Omnionix**. Based on the Qwen2.5 architecture, this model is fine-tuned to balance technical reasoning with a grounded and supportive personality.
**Join the Community:** [Omnionix Discord](https://discord.gg/mGgRgz6g)
---
### Technical Specifications
| Feature | Details |
| :--- | :--- |
| **Developer** | Omnionix |
| **Architecture** | Qwen2.5-1.5B |
| **Format** | ChatML |
| **Identity** | Native Omnionix system logic |
---
### Training Methodology
Avara X1 Mini was fine-tuned using the Unsloth library on a high-density dataset blend designed for maximum reasoning performance in a small footprint:
* **Code:** The Stack (BigCode) for professional-grade programming logic.
* **Mathematics:** Focused math/competition datasets for step-by-step problem solving.
* **Logic:** Open-Platypus for enhanced deductive reasoning and instruction following.
We also have the [LoRA adapter ](https://huggingface.co/Omnionix12345/avara-x1-mini-lora) and the Q4_K_M GGUF: huggingface.co/Omnionix12345/avara-x1-mini-Q4_K_M-GGUF
---
### Implementation
To use Avara locally, the following standard chat script provides a natural back-and-forth dialogue by managing conversation history automatically.
```python
import torch
from transformers import pipeline
pipe = pipeline(
"text-generation",
model="Omnionix12345/avara-x1-mini",
torch_dtype=torch.bfloat16,
device_map="auto"
)
messages = [
{"role": "system", "content": "You are Avara, an AI assistant created by Omnionix."}
]
print("\n--- Avara X1 Mini is Online ---")
while True:
user_input = input("You: ")
if user_input.lower() in ["exit", "quit"]:
break
messages.append({"role": "user", "content": user_input})
outputs = pipe(
messages,
max_new_tokens=512,
do_sample=True,
temperature=0.7
)
assistant_response = outputs[0]["generated_text"][-1]["content"]
print(f"\nAvara: {assistant_response}\n")
messages.append({"role": "assistant", "content": assistant_response})