metadata
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:
┌───────────────────────┐ ┌───────────────┐ ┌──────────────┐
│ 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 was converted to MLX format from EpistemeAI/metatune-gpt20b-R1.1 using mlx-lm version 0.28.4.
Use with mlx
pip install mlx-lm
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)