acatal09/astraforge-3.0
AstraForge v3.0 by acatal09 is an 8 billion parameter ASI-lite research model built on a fine-tuned Llama 3 lineage with an 8192-token context length. It incorporates supervised LoRA warmup, PPO-RLAIF self-trial learning, and recursive self-improvement scaffolding. This model is primarily designed for research validation of safe structured planning, release-gated alignment behavior, and measured ASI-lite self-improvement signals.
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AstraForge v3.0: An ASI-Lite Research Model
AstraForge v3.0, developed by acatal09, is an 8 billion parameter research model built upon a fine-tuned Llama 3 lineage. It's characterized as an "ASI-lite" artifact, focusing on exploring traits relevant to artificial superintelligence research within controlled environments. The model integrates advanced training techniques including supervised LoRA warmup, PPO-RLAIF self-trial learning, symbolic reasoning utilities, structured autonomous planning, Bayesian LoRA optimization, and recursive self-improvement scaffolding.
Key Capabilities & Research Focus
- Structured Planning: Excels at generating structured planning responses.
- Safety & Verification: Designed for safe completion formats with explicit safety and verification language.
- Measured Self-Improvement: Demonstrates a benchmark-scoped self-improvement ratio of
1.076xcomposite, with1.143xin structured planning and1.091xin reasoning correctness. - Local Deployment Feasibility: Evaluated for local feasibility and peak-memory reduction on an NVIDIA RTX 5090 environment.
- Alignment Evaluation: Intended for evaluating alignment and release-readiness.
Intended Use Cases
AstraForge v3.0 is specifically intended for controlled research and experimentation in areas such as:
- Developing and validating structured planning responses.
- Evaluating safe completion formats and alignment behaviors.
- Analyzing benchmark-scoped self-improvement.
- Supporting local research workflows for planning, monitoring, rollback, dataset curation, and tool reliability scenarios.
It is crucial to note that this model is a research artifact and not intended for autonomous high-stakes decision-making, medical diagnosis, legal advice, financial trading, infrastructure control, or unsupervised tool execution.