Instructions to use AIFunOver/SmolLM2-1.7B-Instruct-openvino-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AIFunOver/SmolLM2-1.7B-Instruct-openvino-fp16 with Transformers:

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

pipe = pipeline("text-generation", model="AIFunOver/SmolLM2-1.7B-Instruct-openvino-fp16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("AIFunOver/SmolLM2-1.7B-Instruct-openvino-fp16")
model = AutoModelForMultimodalLM.from_pretrained("AIFunOver/SmolLM2-1.7B-Instruct-openvino-fp16")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AIFunOver/SmolLM2-1.7B-Instruct-openvino-fp16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AIFunOver/SmolLM2-1.7B-Instruct-openvino-fp16"
# 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/SmolLM2-1.7B-Instruct-openvino-fp16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AIFunOver/SmolLM2-1.7B-Instruct-openvino-fp16

SGLang

How to use AIFunOver/SmolLM2-1.7B-Instruct-openvino-fp16 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/SmolLM2-1.7B-Instruct-openvino-fp16" \
    --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/SmolLM2-1.7B-Instruct-openvino-fp16",
		"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/SmolLM2-1.7B-Instruct-openvino-fp16" \
        --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/SmolLM2-1.7B-Instruct-openvino-fp16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AIFunOver/SmolLM2-1.7B-Instruct-openvino-fp16 with Docker Model Runner:
```
docker model run hf.co/AIFunOver/SmolLM2-1.7B-Instruct-openvino-fp16
```

AIFunOver commited on Nov 7, 2024

Commit

cdf4421

verified ·

1 Parent(s): c76f06d

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	@@ -0,0 +1,22 @@

+{
+    "epoch": 2.996074326092646,
+    "eval_logits/chosen": -0.34099623560905457,
+    "eval_logits/rejected": -0.3685227334499359,
+    "eval_logps/chosen": -310.2510070800781,
+    "eval_logps/rejected": -275.43145751953125,
+    "eval_loss": 0.587827205657959,
+    "eval_rewards/accuracies": 0.6746031641960144,
+    "eval_rewards/chosen": 0.01673175022006035,
+    "eval_rewards/margins": 0.5906793475151062,
+    "eval_rewards/rejected": -0.573947548866272,
+    "eval_runtime": 18.8462,
+    "eval_samples": 2000,
+    "eval_samples_per_second": 106.122,
+    "eval_steps_per_second": 3.343,
+    "total_flos": 0.0,
+    "train_loss": 0.5334697115221363,
+    "train_runtime": 7355.3343,
+    "train_samples": 61134,
+    "train_samples_per_second": 24.935,
+    "train_steps_per_second": 0.195
+}