language:

en license: apache-2.0 tags:
Qwen-3-14B
instruct
finetune
reasoning
hybrid-mode
chatml
function calling
tool use
json mode
structured outputs
atropos
dataforge
long context
roleplaying
chat
mlx
3-bit
4-bit
5-bit
6-bit
8-bit
bfloat16
abliterated base_model: NousResearch/Hermes-4-14B library_name: mlx widget:
example_title: Hermes 4 messages:
- role: system content: >- You are Hermes 4, a capable, neutrally-aligned assistant. Prefer concise, correct answers.
- role: user content: Explain the difference between BFS and DFS to a new CS student. pipeline_tag: text-generation model-index:
name: Hermes-4-Qwen-3-14B results: []

mlx-community/NousResearch_Hermes-4-14B-BF16-abliterated-mlx

This model mlx-community/NousResearch_Hermes-4-14B-BF16-abliterated-mlx was converted to MLX format from n0kovo/NousResearch_Hermes-4-14B-BF16-abliterated using mlx-lm version 0.27.1.

Quantizations are also provided in this repo in 8-, 6-, 5-, 4-, 3- and 2-bit.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("mlx-community/NousResearch_Hermes-4-14B-BF16-abliterated-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)

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate model, tokenizer = load("mlx-community/NousResearch_Hermes-4-14B-BF16-abliterated-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)