Instructions to use LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct

SGLang

How to use LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct with Docker Model Runner:
```
docker model run hf.co/LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct
```

Bug fixes for EXAONE across TensorRT-LLM, SGLang, llama.cpp, DeepSpeed, and Transformers

by Bias92 - opened Mar 5

Discussion

Bias92

Mar 5

Hi EXAONE team,

I've been systematically auditing EXAONE model implementations across major inference frameworks and submitted fixes for bugs I found. Here's a summary:

1. NVIDIA/TensorRT-LLM #11862 ✅ Merged

File: exaone_moe_weight_mapper.py
Bug: preprocess_weights() iterates over weights.keys() (a view) while calling pop() and inserting new keys — bug-prone dict mutation pattern that can cause skipped/duplicate keys during MTP weight remapping
Fix: list(weights.keys())

2. DeepSpeed #7853 ✅ Merged

EXAONE 4.0 support for DeepSpeed

3. LGAI-EXAONE Discussion #10 ✅ Resolved

EXAONE 3.5 Transformers v5 compatibility fix (_tied_weights_keys type mismatch, DynamicCache API changes)

4. sgl-project/sglang #19789 (Under Review)

File: exaone4.py
Bug: In forward_split_prefill, aliased tensors are passed to fused_add_rmsnorm — EXAONE4 uses post-LN where hidden_states and residual point to the same tensor object, causing residual += hidden_states to produce 2*hidden_states (CUDA undefined behavior)
Fix: Use single-arg self.model.norm(hidden_states) to match the regular forward path

5. ggml-org/llama.cpp #20076 (Under Review)

File: tensor_mapping.py
Bug: Dead EXAONE3 FFN_DOWN mapping with incorrect prefix model.layers.h.{bid} (should be transformer.h.{bid})
Fix: Remove dead entry, add "exaone" tag to existing correct mapping

Happy to help with any other EXAONE-related issues across the inference ecosystem!

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