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ItsnotAilabs
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Model Details
Model Provider
ItsnotAilabs
Model Tree
EleutherAI/pythia-160m-deduped
Input Modalities
Output Modalities
Supported Functionality
Auro-110M is a small, highly efficient causal language model developed by MedinaMemorySystems for the Sovereign Knowledge Studio. Pruned and distilled from EleutherAI/pythia-160m-deduped, this 110-million parameter model is uniquely optimized for cognitive code generation, creative writing, and specifically for executing the CortexScript DSL.
What makes Auro-110M unique is its ability to run comfortably on standard CPUs with less than 500MB of RAM, making it perfect for lightweight, on-device agentic workflows. It incorporates a CortexScript-aware tokenizer and is fine-tuned to produce structured ReAct tool-call formats and cognitive monologues.
Auro-110M expects a structured prompt containing instructions or ReAct format definitions.
text
System: You are an autonomous agent using CortexScript. User: Write a routine to fetch system time.Agent: <Thought>...</Thought> <Action>...</Action>| Format | Precision | RAM Required | Latency (CPU) |
|---|---|---|---|
| Native | FP32 | ~450 MB | 20 ms/token |
| Half | FP16 | ~220 MB | 15 ms/token |
| Quant | INT8 | ~110 MB | 10 ms/token |
| GGUF | Q4_K_M | ~65 MB | 5 ms/token |
Auro-110M was evaluated on standard benchmarks and a custom CortexScript Execution benchmark.
| Benchmark | Score (%) |
|---|---|
| HellaSwag (10-shot) | 38.2 |
| ARC-Easy (25-shot) | 52.1 |
| CortexScript-Exec (0-shot) | 84.7 |
(Note: CortexScript-Exec is a custom benchmark evaluating the model's ability to syntactically and semantically construct valid CortexScript DSL routines.)
Below is an example of loading Auro-110M using the Hugging Face transformers library to generate a CortexScript routine.
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model_name = "MedinaMemorySystems/Auro-110M" # Load tokenizer and modeltokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float32) prompt = "Write a CortexScript routine to fetch system time and log it."inputs = tokenizer(prompt, return_tensors="pt") # Generate responseoutputs = model.generate( **inputs, max_new_tokens=100, temperature=0.3, do_sample=True, pad_token_id=tokenizer.eos_token_id) print(tokenizer.decode(outputs[0], skip_special_tokens=True))Due to its severely constrained parameter count, Auro-110M struggles with complex zero-shot reasoning outside of its specific fine-tuning distribution (CortexScript and agent monologues). It is prone to hallucinations when queried for deep factual knowledge and should be used strictly for syntactic code generation and lightweight orchestration tasks. It does not contain inherent safety guardrails beyond its limited capability. Use in production requires external content moderation.
bibtex
@misc{medinamemorysystems2026auro110m, title={Auro-110M: A Lightweight Cognitive Language Model for CortexScript}, author={MedinaMemorySystems}, year={2026}, howpublished={\url{https://huggingface.co/MedinaMemorySystems/Auro-110M}}}from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model_name = "MedinaMemorySystems/Auro-110M" # Load tokenizer and modeltokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float32) prompt = "Write a CortexScript routine to fetch system time and log it."inputs = tokenizer(prompt, return_tensors="pt") # Generate responseoutputs = model.generate( **inputs, max_new_tokens=100, temperature=0.3, do_sample=True, pad_token_id=tokenizer.eos_token_id) print(tokenizer.decode(outputs[0], skip_special_tokens=True))@misc{medinamemorysystems2026auro110m, title={Auro-110M: A Lightweight Cognitive Language Model for CortexScript}, author={MedinaMemorySystems}, year={2026}, howpublished={\url{https://huggingface.co/MedinaMemorySystems/Auro-110M}}}System: You are an autonomous agent using CortexScript. User: Write a routine to fetch system time.Agent: <Thought>...</Thought> <Action>...</Action>