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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-8B-Base
+library_name: llama.cpp
+pipeline_tag: text-generation
+---
+# Hermes-Bonsai Karpathy Self-Improving Agent Loop
+
+Stage 2 checkpoint for the Hermes/Bonsai Karpathy auto-research loop.
+
+Last updated: 2026-04-05
+
+This release is inspired by Andrej Karpathy's framing of self-improving training loops and auto-research. It contains the model artifact that worked, plus a concise model card explaining how it was produced and how to run it.
+
+## Overview
+
+- **Base model:** Qwen3-8B-Base
+- **Training method:** supervised fine-tuning via the Hermes/Karpathy loop
+- **Stage:** Stage 2 — the checkpoint that worked
+- **Known limitation:** Stage 3 exposed a learned-helplessness pattern on some tasks; that behavior is documented in the GitHub methodology repo
+- **License:** Apache-2.0 for this release; the underlying base model license also applies to the inherited Qwen3-8B-Base components
+
+## What went into this checkpoint
+
+- The loop-produced training curriculum and trace distillation pipeline
+- 140 verified raw passes used as positive reinforcement for curriculum rebalancing and trace selection
+  - These are Bonsai's own unedited outputs that passed teacher evaluation
+- 10 domains covered across the build
+- Validation signal from a mixed-domain batch
+
+## Domains covered
+
+- memory_integration
+- refusal_redirect
+- self_correction
+- agent_routing
+- devops
+- logic_puzzle
+- code_debugging
+- math
+- architecture
+- research_synthesis
+
+## Strongest domains
+
+Best performance concentrated in:
+- memory_integration
+- refusal_redirect
+- self_correction
+
+## Validation metrics
+
+- Mixed-domain batch: **13/50 raw passes**
+- Raw pass rate: **26%**
+- This checkpoint is the stage 2 model that produced those verified passes
+
+## What's novel
+
+Trained via a graduation protocol with teacher-guided validation, raw-pass reinforcement, and frontier failure analysis. The interesting contribution is the loop methodology; see GitHub for the full curriculum and training workflow.
+
+## GitHub methodology
+
+The training loop, curriculum design, graduation protocol, and detailed methodology live here:
+
+https://github.com/aurous37-lang/Hermes-Bonsai-Self-Improving-Agent-Loop
+
+## Files in this Hugging Face repo
+
+- `bonsai-8b-stage2-post-curriculum-q8.gguf` — the shipped stage 2 checkpoint
+- `README.md` — this model card
+- `LICENSE` — Apache-2.0 license
+
+## How to use
+
+Recommended working config from the stable local run:
+- `--ctx-size 40960`
+- `--n-gpu-layers 37`
+
+### llama.cpp
+
+```bash
+./llama-cli -m bonsai-8b-stage2-post-curriculum-q8.gguf \
+  --ctx-size 40960 \
+  -p "Explain the CAP theorem for a backend engineer."
+```
+
+### llama-server
+
+```bash
+./llama-server -m bonsai-8b-stage2-post-curriculum-q8.gguf \
+  --ctx-size 40960 \
+  --n-gpu-layers 37 \
+  --host 0.0.0.0 --port 8080
+```
+
+Then point your client at the local OpenAI-compatible endpoint exposed by `llama-server`.
+
+## Notes
+
+- This is a release checkpoint, not the full training corpus.
+- The GitHub repo contains the code and documentation needed to reproduce the loop.
+- The Hugging Face repo contains the model artifact that ships from that loop.

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