Instructions to use Jashan887/83_Self_Improving_Loop with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jashan887/83_Self_Improving_Loop with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Jashan887/83_Self_Improving_Loop",
	filename="bonsai-8b-stage2-post-curriculum-q8.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Jashan887/83_Self_Improving_Loop with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Jashan887/83_Self_Improving_Loop
# Run inference directly in the terminal:
llama-cli -hf Jashan887/83_Self_Improving_Loop

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Jashan887/83_Self_Improving_Loop
# Run inference directly in the terminal:
llama-cli -hf Jashan887/83_Self_Improving_Loop

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Jashan887/83_Self_Improving_Loop
# Run inference directly in the terminal:
./llama-cli -hf Jashan887/83_Self_Improving_Loop

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Jashan887/83_Self_Improving_Loop
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Jashan887/83_Self_Improving_Loop

Use Docker

docker model run hf.co/Jashan887/83_Self_Improving_Loop

LM Studio
Jan

vLLM

How to use Jashan887/83_Self_Improving_Loop with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jashan887/83_Self_Improving_Loop"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jashan887/83_Self_Improving_Loop",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jashan887/83_Self_Improving_Loop

Ollama
How to use Jashan887/83_Self_Improving_Loop with Ollama:
```
ollama run hf.co/Jashan887/83_Self_Improving_Loop
```

Unsloth Studio new

How to use Jashan887/83_Self_Improving_Loop with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Jashan887/83_Self_Improving_Loop to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Jashan887/83_Self_Improving_Loop to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Jashan887/83_Self_Improving_Loop to start chatting

Pi new

How to use Jashan887/83_Self_Improving_Loop with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Jashan887/83_Self_Improving_Loop

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Jashan887/83_Self_Improving_Loop"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Jashan887/83_Self_Improving_Loop with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Jashan887/83_Self_Improving_Loop

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Jashan887/83_Self_Improving_Loop

Run Hermes

hermes

Docker Model Runner
How to use Jashan887/83_Self_Improving_Loop with Docker Model Runner:
```
docker model run hf.co/Jashan887/83_Self_Improving_Loop
```

Lemonade

How to use Jashan887/83_Self_Improving_Loop with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Jashan887/83_Self_Improving_Loop

Run and chat with the model

lemonade run user.83_Self_Improving_Loop-{{QUANT_TAG}}

List all available models

lemonade list

83_Self_Improving_Loop / README.md

Jashan887

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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.