Text Generation
MLX
Safetensors
Korean
exaone
korean
gyaru
persona
quantized
non-commercial
conversational
8-bit precision
Instructions to use ChanLumerico/EXAONE-3.5-7.8B-Instruct-Yaho-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ChanLumerico/EXAONE-3.5-7.8B-Instruct-Yaho-8bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("ChanLumerico/EXAONE-3.5-7.8B-Instruct-Yaho-8bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use ChanLumerico/EXAONE-3.5-7.8B-Instruct-Yaho-8bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "ChanLumerico/EXAONE-3.5-7.8B-Instruct-Yaho-8bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "ChanLumerico/EXAONE-3.5-7.8B-Instruct-Yaho-8bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ChanLumerico/EXAONE-3.5-7.8B-Instruct-Yaho-8bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
EXAONE-3.5-7.8B-Instruct-Yaho-8bit 🎀
8-bit MLX quantization of ChanLumerico/EXAONE-3.5-7.8B-Instruct-Yaho
— a Korean gyaru-persona ('갸루귀신') fine-tune of LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct
(LoRA SFT + custom ORPO). Persona, behavior triggers, and the baked 갸루귀신 name anchor are verified to
survive 8-bit quantization. Non-commercial (EXAONE AI Model License 1.1-NC).
The persona anchor is baked into chat_template.jinja — no system prompt needed.
pip install mlx-lm==0.29.1 "transformers==4.57.6"
from mlx_lm import load, generate
model, tok = load("ChanLumerico/EXAONE-3.5-7.8B-Instruct-Yaho-8bit")
p = tok.apply_chat_template([{"role": "user", "content": "오늘 시험 망쳤어…"}],
add_generation_prompt=True, tokenize=False)
print(generate(model, tok, prompt=p, max_tokens=512))
See the full model card / eval / interpretability and the project repo. Base: EXAONE-3.5 (LG AI Research).
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Model size
8B params
Tensor type
F32
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U32 ·
Hardware compatibility
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8-bit
Model tree for ChanLumerico/EXAONE-3.5-7.8B-Instruct-Yaho-8bit
Base model
LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct