Instructions to use AIFunOver/SmolLM2-360M-Instruct-openvino-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AIFunOver/SmolLM2-360M-Instruct-openvino-4bit with Transformers:

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

pipe = pipeline("text-generation", model="AIFunOver/SmolLM2-360M-Instruct-openvino-4bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AIFunOver/SmolLM2-360M-Instruct-openvino-4bit")
model = AutoModelForCausalLM.from_pretrained("AIFunOver/SmolLM2-360M-Instruct-openvino-4bit", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AIFunOver/SmolLM2-360M-Instruct-openvino-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AIFunOver/SmolLM2-360M-Instruct-openvino-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AIFunOver/SmolLM2-360M-Instruct-openvino-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AIFunOver/SmolLM2-360M-Instruct-openvino-4bit

SGLang

How to use AIFunOver/SmolLM2-360M-Instruct-openvino-4bit 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 "AIFunOver/SmolLM2-360M-Instruct-openvino-4bit" \
    --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": "AIFunOver/SmolLM2-360M-Instruct-openvino-4bit",
		"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 "AIFunOver/SmolLM2-360M-Instruct-openvino-4bit" \
        --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": "AIFunOver/SmolLM2-360M-Instruct-openvino-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AIFunOver/SmolLM2-360M-Instruct-openvino-4bit with Docker Model Runner:
```
docker model run hf.co/AIFunOver/SmolLM2-360M-Instruct-openvino-4bit
```

SmolLM2-360M-Instruct-openvino-4bit / all_results.json

AIFunOver

Upload all_results.json with huggingface_hub

e5e4f23 verified almost 2 years ago

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{
    "epoch": 1.9973828840617638,
    "eval_logits/chosen": -1.6407532691955566,
    "eval_logits/rejected": -1.6968854665756226,
    "eval_logps/chosen": -375.6463623046875,
    "eval_logps/rejected": -323.7197570800781,
    "eval_loss": 0.6348475217819214,
    "eval_rewards/accuracies": 0.6190476417541504,
    "eval_rewards/chosen": -0.034213583916425705,
    "eval_rewards/margins": 0.3567626178264618,
    "eval_rewards/rejected": -0.3909761905670166,
    "eval_runtime": 22.3598,
    "eval_samples": 2000,
    "eval_samples_per_second": 89.446,
    "eval_steps_per_second": 2.818,
    "total_flos": 0.0,
    "train_loss": 0.6516540072998911,
    "train_runtime": 5944.7081,
    "train_samples": 61134,
    "train_samples_per_second": 20.568,
    "train_steps_per_second": 0.16
}

	{
	"epoch": 1.9973828840617638,
	"eval_logits/chosen": -1.6407532691955566,
	"eval_logits/rejected": -1.6968854665756226,
	"eval_logps/chosen": -375.6463623046875,
	"eval_logps/rejected": -323.7197570800781,
	"eval_loss": 0.6348475217819214,
	"eval_rewards/accuracies": 0.6190476417541504,
	"eval_rewards/chosen": -0.034213583916425705,
	"eval_rewards/margins": 0.3567626178264618,
	"eval_rewards/rejected": -0.3909761905670166,
	"eval_runtime": 22.3598,
	"eval_samples": 2000,
	"eval_samples_per_second": 89.446,
	"eval_steps_per_second": 2.818,
	"total_flos": 0.0,
	"train_loss": 0.6516540072998911,
	"train_runtime": 5944.7081,
	"train_samples": 61134,
	"train_samples_per_second": 20.568,
	"train_steps_per_second": 0.16
	}