MLX conversion for mlx-lm
According to the model card, the weights were converted to MLX format with mlx-lm 0.29.1 and load through the documented mlx_lm load and generate calls after pip install mlx-lm.
Open Source Model Profile · isetnefret
DarkIdol-Llama-3.1-8B-Instruct-1.3-Uncensored-mlx-fp16 is an 8.03B-parameter Llama MLX conversion from isetnefret with a documented mlx-lm loading workflow.
DarkIdol-Llama-3.1-8B-Instruct-1.3-Uncensored-mlx-fp16 is published by isetnefret as an MLX-format Llama text-generation model. The captured configuration identifies LlamaForCausalLM and Safetensors metadata reports 8,030,261,248 parameters. According to the model card, it converts aifeifei798/DarkIdol-Llama-3.1-8B-Instruct-1.3-Uncensored with mlx-lm 0.29.1, and card data records llama3.1 licensing.
According to the model card, the weights were converted to MLX format with mlx-lm 0.29.1 and load through the documented mlx_lm load and generate calls after pip install mlx-lm.
According to the model card, the source is aifeifei798/DarkIdol-Llama-3.1-8B-Instruct-1.3-Uncensored, and hub tags record roleplay, llama3, and sillytavern associations.
According to the model card, the usage snippet covers loading the repository id, applying an available chat template, and calling generate with verbose output.
Source: isetnefret/DarkIdol-Llama-3.1-8B-Instruct-1.3-Uncensored-mlx-fp16
Captured: Unknown. Processed: 2026-09-07T19:35:57.011751+00:00.
isetnefret/DarkIdol-Llama-3.1-8B-Instruct-1.3-Uncensored-mlx-fp16 The Model isetnefret/DarkIdol-Llama-3.1-8B-Instruct-1.3-Uncensored-mlx-fp16 was converted to MLX format from aifeifei798/DarkIdol-Llama-3.1-8B-Instruct-1.3-Uncensored using mlx-lm version 0.29.1 . Use with mlx pip install mlx-lm from mlx_lm import load, generate model, tokenizer = load( "isetnefret/DarkIdol-Llama-3.1-8B-Instruct-1.3-Uncensored-mlx-fp16" ) 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…
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