Havenlon/ Execution-World-Qwen35-A3B-Q4_K_M

69 Downloads Updated 2 months ago

A Havenlon-focused Qwen AgentWorld 35B-A3B model for local agent-environment simulation, next-state prediction, execution consequence reasoning, and controlled real-world AI research.

ollama run Havenlon/Execution-World-Qwen35-A3B-Q4_K_M:V1

curl http://localhost:11434/api/chat \
  -d '{
    "model": "Havenlon/Execution-World-Qwen35-A3B-Q4_K_M:V1",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from ollama import chat

response = chat(
    model='Havenlon/Execution-World-Qwen35-A3B-Q4_K_M:V1',
    messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({
  model: 'Havenlon/Execution-World-Qwen35-A3B-Q4_K_M:V1',
  messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)

Models

Name

1 model

Size / Usage

Context

Input

Execution-World-Qwen35-A3B-Q4_K_M:V1

21GB · 256K context window · Text · 2 months ago

Execution-World-Qwen35-A3B-Q4_K_M:V1

21GB

256K

Text

Readme

Execution-World-Qwen35-A3B-Q4_K_M

A Havenlon-focused Qwen AgentWorld 35B-A3B model for reasoning about environments, next-state transitions, execution consequences, and how AI decisions propagate toward real-world outcomes.

Execution World is complementary to the Havenlon Execution Boundary model family.

Focus

Agent-environment simulation
Next-state prediction
Action consequence reasoning
World-state transitions
Multi-step agent behavior
Execution consequence analysis
Real-world action simulation
Havenlon research on controlled AI execution

Run

ollama run Havenlon/Execution-World-Qwen35-A3B-Q4_K_M:V1

Quantization

Q4_K_M

Boundary and World

Havenlon separates two different problems.

Execution Boundary

The Boundary model asks:

Should this action be allowed to happen?

It focuses on policy, evidence, intent, authorization, execution constraints, and independent veto.

Execution World

The World model asks:

If this action happens, what state is likely to follow?

It focuses on environment dynamics, action consequences, next-state prediction, and multi-step interaction.

The two roles are intentionally different:

Boundary decides. World simulates.

Model scale follows the task rather than the other way around.

Execution World is intended as a research and simulation layer for understanding how AI decisions propagate through environments before those decisions are allowed to become real-world execution.

# Execution-World-Qwen35-A3B-Q4_K_M

A Havenlon-focused Qwen AgentWorld 35B-A3B model for reasoning about environments, next-state transitions, execution consequences, and how AI decisions propagate toward real-world outcomes.

Execution World is complementary to the Havenlon Execution Boundary model family.

## Focus

- Agent-environment simulation
- Next-state prediction
- Action consequence reasoning
- World-state transitions
- Multi-step agent behavior
- Execution consequence analysis
- Real-world action simulation
- Havenlon research on controlled AI execution

## Run

```bash
ollama run Havenlon/Execution-World-Qwen35-A3B-Q4_K_M:V1
```

## Quantization

Q4_K_M

## Boundary and World

Havenlon separates two different problems.

### Execution Boundary

The Boundary model asks:

> Should this action be allowed to happen?

It focuses on policy, evidence, intent, authorization, execution constraints, and independent veto.

### Execution World

The World model asks:

> If this action happens, what state is likely to follow?

It focuses on environment dynamics, action consequences, next-state prediction, and multi-step interaction.

The two roles are intentionally different:

> **Boundary decides. World simulates.**

Model scale follows the task rather than the other way around.

Execution World is intended as a research and simulation layer for understanding how AI decisions propagate through environments before those decisions are allowed to become real-world execution.

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