Instructions to use hyper-accel/tiny-random-exaone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hyper-accel/tiny-random-exaone with Transformers:

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

pipe = pipeline("text-generation", model="hyper-accel/tiny-random-exaone", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("hyper-accel/tiny-random-exaone", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hyper-accel/tiny-random-exaone with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/hyper-accel/tiny-random-exaone

SGLang

How to use hyper-accel/tiny-random-exaone 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 "hyper-accel/tiny-random-exaone" \
    --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": "hyper-accel/tiny-random-exaone",
		"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 "hyper-accel/tiny-random-exaone" \
        --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": "hyper-accel/tiny-random-exaone",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hyper-accel/tiny-random-exaone with Docker Model Runner:
```
docker model run hf.co/hyper-accel/tiny-random-exaone
```

tiny-random-exaone / config.json

minho

Upload tiny-random exaone model

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	{
	"_attn_implementation_autoset": false,
	"_name_or_path": "LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct",
	"activation_function": "silu",
	"architectures": [
	"ExaoneForCausalLM"
	],
	"attention_dropout": 0.0,
	"auto_map": {
	"AutoConfig": "configuration_exaone.ExaoneConfig",
	"AutoModelForCausalLM": "modeling_exaone.ExaoneForCausalLM",
	"AutoModelForSequenceClassification": "models.exaone.modeling_exaone.ExaoneForSequenceClassification"
	},
	"bos_token_id": 1,
	"embed_dropout": 0.0,
	"eos_token_id": 361,
	"hidden_size": 1024,
	"initializer_range": 0.02,
	"intermediate_size": 3584,
	"layer_norm_epsilon": 1e-05,
	"max_position_embeddings": 4096,
	"model_type": "exaone",
	"num_attention_heads": 8,
	"num_key_value_heads": 2,
	"num_layers": 2,
	"pad_token_id": 0,
	"rope_scaling": null,
	"rope_theta": 500000.0,
	"tie_word_embeddings": false,
	"torch_dtype": "float32",
	"transformers_version": "4.44.0",
	"use_cache": true,
	"vocab_size": 102400
	}