Instructions to use abersbail/smollm-135m-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abersbail/smollm-135m-instruct with Transformers:

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

pipe = pipeline("text-generation", model="abersbail/smollm-135m-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("abersbail/smollm-135m-instruct")
model = AutoModelForCausalLM.from_pretrained("abersbail/smollm-135m-instruct", 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 abersbail/smollm-135m-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "abersbail/smollm-135m-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "abersbail/smollm-135m-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/abersbail/smollm-135m-instruct

SGLang

How to use abersbail/smollm-135m-instruct 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 "abersbail/smollm-135m-instruct" \
    --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": "abersbail/smollm-135m-instruct",
		"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 "abersbail/smollm-135m-instruct" \
        --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": "abersbail/smollm-135m-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use abersbail/smollm-135m-instruct with Docker Model Runner:
```
docker model run hf.co/abersbail/smollm-135m-instruct
```

⚡ SmolLM-135M-Instruct Deployed Model

This repository contains the SmolLM-135M-Instruct lightweight LLM (~135M parameters, ~250MB weight files), deployed on Hugging Face Hub by abersbail.

📊 Model Summary

Model Name: SmolLM-135M-Instruct
Parameters: 135 Million (~100MB-class ultra-compact LLM)
Architecture: LlamaForCausalLM / SmolLM
Context Window: 2048 tokens
License: Apache 2.0

🚀 Usage Guide

1. Python `transformers`

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "abersbail/smollm-135m-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

messages = [{"role": "user", "content": "What is artificial intelligence?"}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=100, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

2. cURL (Hugging Face Serverless Inference API)

curl https://api-inference.huggingface.co/models/abersbail/smollm-135m-instruct   -X POST   -H "Authorization: Bearer hf_..."   -H "Content-Type: application/json"   -d '{"inputs": "Hello! Tell me a fun fact."}'

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Safetensors

Model size

0.1B params

Tensor type

BF16

Model tree for abersbail/smollm-135m-instruct

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HuggingFaceTB/SmolLM-135M

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this model

abersbail
/

smollm-135m-instruct

⚡ SmolLM-135M-Instruct Deployed Model

📊 Model Summary

🚀 Usage Guide

1. Python `transformers`

2. cURL (Hugging Face Serverless Inference API)

Model tree for abersbail/smollm-135m-instruct

Space using abersbail/smollm-135m-instruct 1

⚡ SmolLM-135M-Instruct Deployed Model

📊 Model Summary

🚀 Usage Guide

1. Python transformers

2. cURL (Hugging Face Serverless Inference API)

Model tree for abersbail/smollm-135m-instruct

Space using abersbail/smollm-135m-instruct 1

1. Python `transformers`