Instructions to use AssistantsLab/SmolLM2-360M-humanized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AssistantsLab/SmolLM2-360M-humanized with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AssistantsLab/SmolLM2-360M-humanized")
model = AutoModelForCausalLM.from_pretrained("AssistantsLab/SmolLM2-360M-humanized")
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

vLLM

How to use AssistantsLab/SmolLM2-360M-humanized with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AssistantsLab/SmolLM2-360M-humanized

SGLang

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

Docker Model Runner
How to use AssistantsLab/SmolLM2-360M-humanized with Docker Model Runner:
```
docker model run hf.co/AssistantsLab/SmolLM2-360M-humanized
```

Michielo commited on Feb 10, 2025

Commit

2c2fe6e

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@@ -15,7 +15,7 @@ tags:
 ---
-# SmolLM2-135M-Humanized
 ##  Table of Contents
@@ -27,11 +27,11 @@ tags:
 ## Model Summary
-**SmolLM2-135M-Humanized** is a fine-tuned version of the [SmolLM2-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct) model, optimized using the Direct Preference Optimization (DPO) method. To do this we used the "[Human-Like-DPO-Dataset](https://huggingface.co/datasets/HumanLLMs/Human-Like-DPO-Dataset)" from [Human-Like LLMs](https://huggingface.co/HumanLLMs).
 Unlike traditional fine-tuning datasets that aim to improve specific benchmarks or metrics, the Human-Like-DPO-Dataset focuses on aligning the model's behavior with human preferences. This process enhances the model's ability to generate more natural, human-like responses, making it particularly well-suited for conversational applications.
-By emphasizing response quality and relatability, SmolLM2-135M-Humanized is designed to deliver an engaging and intuitive user experience in dialogue-based scenarios.
 ### How to use

 ---
+# SmolLM2-360M-Humanized
 ##  Table of Contents
 ## Model Summary
+**SmolLM2-360M-Humanized** is a fine-tuned version of the [SmolLM2-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct) model, optimized using the Direct Preference Optimization (DPO) method. To do this we used the "[Human-Like-DPO-Dataset](https://huggingface.co/datasets/HumanLLMs/Human-Like-DPO-Dataset)" from [Human-Like LLMs](https://huggingface.co/HumanLLMs).
 Unlike traditional fine-tuning datasets that aim to improve specific benchmarks or metrics, the Human-Like-DPO-Dataset focuses on aligning the model's behavior with human preferences. This process enhances the model's ability to generate more natural, human-like responses, making it particularly well-suited for conversational applications.
+By emphasizing response quality and relatability, SmolLM2-360M-Humanized is designed to deliver an engaging and intuitive user experience in dialogue-based scenarios.
 ### How to use