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Omnionix12345
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Omnionix12345
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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.
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| Feature | Details |
|---|---|
| Developer | Omnionix |
| Architecture | Qwen2.5-1.5B |
| Format | ChatML |
| Identity | Native Omnionix system logic |
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:
We also have the LoRA adapter and the Q4_K_M GGUF: huggingface.co/Omnionix12345/avara-x1-mini-Q4_K_M-GGUF
To use Avara locally, the following standard chat script provides a natural back-and-forth dialogue by managing conversation history automatically.
python
import torchfrom 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})import torchfrom 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})