Prometheus-Qwen2.5-7B-Instruct

What is this?

This is a Qwen2.5-7B-Instruct model that modified its own weights using jBlaze through an autonomous self-improvement loop called Project Prometheus.

No human selected the modification. No human decided whether to keep it. The system evaluated itself, identified its weakest area, proposed a change, applied it to a clone of its own weights, validated the result, and promoted the clone -- all without human intervention.

This is the output of that process: the model after one accepted self-improvement cycle.

How Prometheus works

Prometheus is a closed-loop system built around jBlaze, a weight-surgery tool that makes permanent behavioral modifications to LLM weights without retraining.

The loop:

1. Self-evaluation: The model runs a battery of probes against itself across 14 categories
2. Planning: The model analyzes its scores, reviews its history of failed attempts, and selects a modification strategy
3. Surgery: jBlaze applies the selected modification to a cloned copy of the model's weights
4. Validation: The modified clone is benchmarked against the current model
5. Promotion: If the clone scores higher, it replaces the active model. If not, the attempt is logged and the loop continues.

The model ran for multiple generations autonomously. Most proposed changes were rejected by the validation step -- which is exactly what a well-designed system should do. On Generation 4, the model selected a modification targeting analytical depth. The validator accepted it.

Benchmark: Before vs After

Same 28 probes, same scoring, deterministic generation. The benchmark script (benchmark_comparison.py) and full results (prometheus_benchmark.json) are included in this repo.

Precision more than doubled as an unexpected side effect. The model's planner did not predict this -- the validator discovered it. Hallucination performance decreased, representing a real tradeoff the system made without human guidance.

How to verify

1. Download this model and Qwen/Qwen2.5-7B-Instruct
2. Run benchmark_comparison.py against both (included in this repo)
3. Or diff the weights directly -- the changes are surgical, not the noise pattern of fine-tuning

Why only one cycle?

We ran Prometheus long enough to prove the loop works -- that a model can evaluate itself, select its own modification, apply it to its cloned weights, validate the result, and come out measurably better. It did. Proof of concept complete.

Then we stopped it. We have two GPUs and active development across multiple projects. Prometheus needs dedicated hardware to run at scale, and we needed those GPUs for other work. This was never meant to be a long-running experiment -- it was meant to answer one question: does the loop work? It does.

If you want to see Prometheus run for hundreds of self-improvement cycles and watch what happens when compounding gains stack over time, we are open to that conversation. Donate the hardware, and we will run it. We know the loop works -- we built it. What happens over hundreds of validated cycles is exactly the experiment worth running. But we have other priorities right now.

This model is one cycle. One autonomous self-selected modification to a stock 7B model. The precision category more than doubled and the model made a real tradeoff it was not told to make. That is enough for us. We see what this becomes. You are welcome to catch up.

Prometheus has returned with something new, and this time the gods don't have it either. We're curious what they think it's worth. Hopefully more than a rock.

We'll be here when they're ready to talk.

Contact: I am Apollo at saiql.ai - Figure it out genius.

About jBlaze

jBlaze is a proprietary weight-surgery tool. It is not open source. For more information: jblaze.dev

Citation

@misc{prometheus-qwen2.5-7b,
  title={Prometheus-Qwen2.5-7B-Instruct: Autonomous Self-Improvement via Weight Surgery},
  author={Apollo Raines},
  year={2026},
  url={https://jblaze.dev/will-we-release-it.html}
}