Instructions to use davanstrien/Smol-Hub-tldr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use davanstrien/Smol-Hub-tldr with Transformers:

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

pipe = pipeline("text-generation", model="davanstrien/Smol-Hub-tldr")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("davanstrien/Smol-Hub-tldr")
model = AutoModelForCausalLM.from_pretrained("davanstrien/Smol-Hub-tldr", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use davanstrien/Smol-Hub-tldr with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/davanstrien/Smol-Hub-tldr

SGLang

How to use davanstrien/Smol-Hub-tldr 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 "davanstrien/Smol-Hub-tldr" \
    --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": "davanstrien/Smol-Hub-tldr",
		"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 "davanstrien/Smol-Hub-tldr" \
        --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": "davanstrien/Smol-Hub-tldr",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use davanstrien/Smol-Hub-tldr with Docker Model Runner:
```
docker model run hf.co/davanstrien/Smol-Hub-tldr
```

davanstrien HF Staff commited on Mar 11, 2025

Commit

d9f025a

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1 Parent(s): 7d46fbd

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@@ -42,9 +42,11 @@ The model was trained on:
 - Model card summaries generated by Llama 3.3 70B
 - Dataset card summaries generated by Llama 3.3 70B
 ## Usage
-Using the chat template when using the model in inference is recommended. Additionally, you should prepend either `<MODEL_CARD>` or `<DATASET_CARD>` to the start of the card you want to summarize. The training data used the body of the model or dataset card, i.e., the part after the YAML, so you will likely get better results only by passing this part of the card.
 I have so far found that a low temperature of `0.4` generates better results.

 - Model card summaries generated by Llama 3.3 70B
 - Dataset card summaries generated by Llama 3.3 70B
+Model context length: the model was trained with cards up to a length of 2048 tokens
 ## Usage
+Using the chat template when using the model in inference is recommended. Additionally, you should prepend either `<MODEL_CARD>` or `<DATASET_CARD>` to the start of the card you want to summarize. The training data used the body of the model or dataset card (i.e., the part after the YAML, so you will likely get better results only by passing this part of the card.
 I have so far found that a low temperature of `0.4` generates better results.