library_name: transformers
license: apache-2.0
language:
- en
tags: - creative
- creative writing
- fiction writing
- plot generation
- sub-plot generation
- story generation
- scene continue
- storytelling
- fiction story
- science fiction
- romance
- all genres
- llama 3.1
- llama-3
- llama3
- llama-3.1
- story
- writing
- vivid prosing
- vivid writing
- fiction
- roleplaying
- bfloat16
- swearing
- role play
- sillytavern
- backyard
- horror
- context 128k
- mergekit
- merge
- 6X8B
- moe
- mixture of experts
- not-for-all-audiences
- mlx
- mlx-my-repo
- mlx
- mlx-my-repo
base_model: derek-wh/L3.1-MOE-6X8B-Dark-Reasoning-Dantes-Peak-Hermes-R1-Uncensored-36B-mlx-8Bit
pipeline_tag: text-generation
metalman3289/L3.1-MOE-6X8B-Dark-Reasoning-Dantes-Peak-Hermes-R1-Uncensored-36B-mlx-8Bit-mlx-4Bit
The Model metalman3289/L3.1-MOE-6X8B-Dark-Reasoning-Dantes-Peak-Hermes-R1-Uncensored-36B-mlx-8Bit-mlx-4Bit was converted to MLX format from derek-wh/L3.1-MOE-6X8B-Dark-Reasoning-Dantes-Peak-Hermes-R1-Uncensored-36B-mlx-8Bit using mlx-lm version 0.31.2.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("metalman3289/L3.1-MOE-6X8B-Dark-Reasoning-Dantes-Peak-Hermes-R1-Uncensored-36B-mlx-8Bit-mlx-4Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)