LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct-AWQ

text-generation model by LGAI-EXAONE

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We introduce EXAONE 3.5, a collection of instruction-tuned bilingual (English and Korean) generative models ranging from 2.4B to 32B parameters, developed and released by LG AI Research. EXAONE 3.5 language models include: 1) 2.4B model optimized for deployment on small or resource-constrained devices, 2) 7.8B model matching the size of its predecessor but offering improved performance, and 3) 32B model delivering powerful performance. All models support long-context processing of up to 32K tokens. Each model demonstrates state-of-the-art performance in real-world use cases and long-context understanding, while remaining competitive in general domains compared to recently released models of similar sizes. For more details, please refer to our technical report, blog and GitHub. This repository contains the AWQ-quantized weights of the instruction-tuned 7.8B language model with the following features: We recommend to use transformers>=4.43 and autoawq>=0.2.7.post3. Here is the code snippet to run conversational inference with the model: EXAONE 3.5 models can…

LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct-AWQ on Hugging Face A short preview, not the full document.

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  1. EXAONE-3.5-7.8B-Instruct-AWQ
  2. Introduction
  3. Quickstart
  4. Deployment
  5. Quantization
  6. Limitation
  7. License
  8. Citation
  9. Contact

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Model overview

LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct-AWQ is a text-generation model repository published by LGAI-EXAONE on Hugging Face. The source reports the transformers library.

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Model facts

Task
text-generation
Library
transformers
Recent downloads (30 days)
921,196
Cumulative likes
18
Hugging Face trending score
0
Reported safetensors parameters
7,818,448,896
Architecture
ExaoneForCausalLM
License
other
Access
Not gated by Hugging Face
Created
2024-12-01T13:16:55.000Z
Last modified
2026-02-06T06:10:28.000Z

Compatibility and lineage

Reported languages: en, ko

Reported base models: EXAONE-3.5-7.8B-Instruct

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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Source: Hugging Face Hub. Metadata observed 2026-09-30T06:39:13.322Z. Daily imports are snapshots, not real-time monitoring.

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