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sinbal/EXAONE-Deep-7.8B-MLX-8bit
EXAONE-Deep-7.8B-MLX-8bit is a text generation model from sinbal. Use it when you need the model to write or continue text. It is set up for mlx. The card lists the license as other.
MLX-converted and 8-bit quantized version of LGAI-EXAONE/EXAONE-Deep-7.8B, optimized for Apple Silicon Macs.
Downloads · 30 days
16
6% of all-time downloads
All-time downloads
267
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7.8B
8.3 GB on disk
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.safetensors8.3 GB · 100%
How the weights are stored.
U327.8B · 100%
From the Hugging Face model README
MLX-converted and 8-bit quantized version of LGAI-EXAONE/EXAONE-Deep-7.8B, optimized for Apple Silicon Macs.
EXAONE Deep is a reasoning-enhanced language model developed by LG AI Research. It exhibits superior capabilities in various reasoning tasks including math, coding, and analysis.
| Spec | Value |
|---|---|
| Original Model | LGAI-EXAONE/EXAONE-Deep-7.8B |
| Quantization | 8-bit (8.5 bits per weight) |
| Framework | MLX (Apple Silicon native) |
| Size | ~7.7GB (reduced from ~16GB) |
| Languages | English, Korean |
Tested on M2 Max 32GB:
| Metric | Value |
|---|---|
| Load time | ~1-2s |
| Generation | ~25-35 tok/s |
| Memory usage | ~8GB |
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("sinbal/EXAONE-Deep-7.8B-MLX-8bit")
prompt = "Explain the key factors for AI investment in 2025."
messages = [{"role": "user", "content": prompt}]
formatted = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=formatted, max_tokens=500)
print(response)
MLX provides explicit memory control - ideal for resource-constrained environments:
# Explicit cleanup when done
del model, tokenizer
import gc; gc.collect()
import mlx.core as mx; mx.clear_cache()
Converted using mlx-lm version 0.28.3:
mlx_lm.convert \
--hf-path LGAI-EXAONE/EXAONE-Deep-7.8B \
-q \
--q-bits 8 \
--mlx-path ./EXAONE-Deep-7.8B-MLX-8bit
This model follows the EXAONE AI Model License Agreement 1.1. Please refer to the original model's license for terms of use.