---
base_model: EpistemeAI/metatune-gpt20b-R1.1
tags:
- text-generation-inference
- transformers
- unsloth
- gpt_oss
- mlx
license: apache-2.0
language:
- en
datasets:
- EpistemeAI/recursive_self_improvement_dataset
library_name: mlx
pipeline_tag: text-generation
---

# metatune-gpt20b-R1.1-qx86-hi-mlx

Metrics aren't everything. The qx86-hi is more fun than q8-hi. Enjoy the quant.

Sample output:

```bash
┌───────────────────────┐        ┌───────────────┐          ┌──────────────┐
│  UI / CLI              │<------>|  Agent        |<-------->|  Provider     │
└───────────────────────┘        ├───────────────┤          └──────────────┘
                                  │  Thread work  │
                                  │  HTTP, FILE,  │
                                  │  TOOL, etc.   │
                                  └───────┬───────┘
                                          │ DB logging (SQLite)
                                          ▼
                              ┌───────────────────────┐
                              │  SQLite LOG DB        │
                              └───────────────────────┘

                                   ┌───────────────────────────────┐
                                   │  PostgreSQL (orchestrator)    │
                                   └───────────────────────────────┘
                        +---------------------------+   +--------------------------+
                        |  PL/Perl functions        |   |  Tables & triggers      |
                        +---------------------------+   +--------------------------+

```
It's really hard to line up ASCII code, but looked good on screen

This model [metatune-gpt20b-R1.1-qx86-hi-mlx](https://huggingface.co/nightmedia/metatune-gpt20b-R1.1-qx86-hi-mlx) was
converted to MLX format from [EpistemeAI/metatune-gpt20b-R1.1](https://huggingface.co/EpistemeAI/metatune-gpt20b-R1.1)
using mlx-lm version **0.28.4**.

## Use with mlx

```bash
pip install mlx-lm
```

```python
from mlx_lm import load, generate

model, tokenizer = load("metatune-gpt20b-R1.1-qx86-hi-mlx")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)
```