Hoodx/mira-agent-instinct

Mira AI Agent Model – Instinctive AGI / RSI

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Mira is an agent-oriented LLM with instinctive reasoning compression and safe recursive self-improvement loops. Designed for long-horizon tasks, tool use, and self-play fine-tuning with eval gating.

Overview

• —Instinct mode: fast heuristic policy that reduces reasoning tokens while preserving first-attempt accuracy.
• —Agent layer: smolagents / Transformers Agent Toolkit compatible.
• —RSI: offline self-play fine-tuning with DPO, frozen improvement operator, lineage tracking.
• —Safety: gated repo, guardrails, audit log, explicit limitations.

Model Details

• —Base model: Qwen/Qwen3-8B
• —Architecture: decoder-only transformer
• —Context length: 32k
• —Training: supervised fine-tune on agent trajectories + self-play synthetic data
• —Quantized variants: Q4KM, Q8_0

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Hoodx/mira-agent-instinct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

messages = [
    {"role": "user", "content": "Plan a 3-step research workflow and execute step 1"}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")
outputs = model.generate(**inputs)

smolagents

from smolagents import CodeAgent, HfApiModel

model = HfApiModel(model_id="Hoodx/mira-agent-instinct")
agent = CodeAgent(model=model, tools=[...])
agent.run("...")

Demo

See the HuggingFace Space: Hoodx/mira-agent-instinct-demo

Evaluation

Results in .eval_results/:

• —Terminal-Bench 2.1
• —SWE-Bench Verified
• —AgentBench

Limitations

• —Not AGI. Agent-oriented LLM with heuristic compression.
• —Recursive self-improvement is offline and eval-gated.
• —Tool use requires guardrails.

Citation

@misc{mira-agent-instinct,
  title={Mira AI Agent Model – Instinctive AGI / RSI},
  author={Hoodx},
  year={2026}
}

License

Apache-2.0