Instructions to use etanlightstone/simple-lm-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use etanlightstone/simple-lm-v2 with Transformers:

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

pipe = pipeline("text-generation", model="etanlightstone/simple-lm-v2", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("etanlightstone/simple-lm-v2", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use etanlightstone/simple-lm-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "etanlightstone/simple-lm-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "etanlightstone/simple-lm-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/etanlightstone/simple-lm-v2

SGLang

How to use etanlightstone/simple-lm-v2 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 "etanlightstone/simple-lm-v2" \
    --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": "etanlightstone/simple-lm-v2",
		"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 "etanlightstone/simple-lm-v2" \
        --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": "etanlightstone/simple-lm-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use etanlightstone/simple-lm-v2 with Docker Model Runner:
```
docker model run hf.co/etanlightstone/simple-lm-v2
```

simple-lm-v2 / config.json

etanlightstone

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	{
	"activation": "gelu",
	"architectures": [
	"_SimpleLMForCausalLM"
	],
	"auto_map": {
	"AutoConfig": "configuration_simple_lm.SimpleLMConfig",
	"AutoModelForCausalLM": "modeling_simple_lm.SimpleLMForCausalLM"
	},
	"bias": true,
	"bos_token_id": 1,
	"context_length": 512,
	"d_ff": 2048,
	"d_model": 768,
	"dropout": 0.1,
	"dtype": "float32",
	"eos_token_id": 2,
	"model_type": "simple_lm",
	"n_heads": 8,
	"n_layers": 12,
	"pad_token_id": 2,
	"tie_word_embeddings": true,
	"transformers_version": "5.8.0",
	"vocab_size": 32000
	}