Instructions to use prithivMLmods/SmolLM2-Rethink-135M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/SmolLM2-Rethink-135M with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/SmolLM2-Rethink-135M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/SmolLM2-Rethink-135M")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/SmolLM2-Rethink-135M")
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 prithivMLmods/SmolLM2-Rethink-135M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/SmolLM2-Rethink-135M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/SmolLM2-Rethink-135M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/SmolLM2-Rethink-135M

SGLang

How to use prithivMLmods/SmolLM2-Rethink-135M 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 "prithivMLmods/SmolLM2-Rethink-135M" \
    --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": "prithivMLmods/SmolLM2-Rethink-135M",
		"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 "prithivMLmods/SmolLM2-Rethink-135M" \
        --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": "prithivMLmods/SmolLM2-Rethink-135M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/SmolLM2-Rethink-135M with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/SmolLM2-Rethink-135M
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SmolLM2-Rethink-135M

SmolLM2-Rethink-135M is an experimental lightweight model trained on the Celestia3-DeepSeek-R1-0528 reasoning dataset. Based on the SmolLM2-135M-Instruct architecture, this model is specifically optimized for reasoning, structured outputs, and efficient small-scale deployment. Despite its compact size (135M parameters), it demonstrates strong capabilities in logical deduction, conversational coherence, and lightweight inference tasks.

Key Highlights

Compact & Efficient Lightweight architecture (135M) suitable for fast inference, mobile applications, and edge deployment.
Reasoning-Centric Training Fine-tuned on high-quality reasoning and instruction datasets like Celestia3-DeepSeek-R1-0528, focusing on multi-step logical thinking.
Low-Resource Optimization Designed to run effectively on CPUs or single-GPU setups with minimal memory footprint.
Structured Outputs Supports generation of clean, structured content including lists, steps, tables, and JSON-like responses.

Quickstart with 🤗 Transformers

%%capture
!pip install transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

checkpoint = "prithivMLmods/SmolLM2-Rethink-135M"
device = "cuda"  # or "cpu"

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)

messages = [{"role": "user", "content": "What is gravity?"}]
input_text = tokenizer.apply_chat_template(messages, tokenize=False)
print(input_text)

inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(
    inputs,
    max_new_tokens=1024,
    temperature=0.2,
    top_p=0.9,
    do_sample=True
)

print(tokenizer.decode(outputs[0]))

Intended Use

Instruction Following & QA Good for answering simple questions, following short instructions, and general user interactions.
Educational Tools Suitable for lightweight tutoring bots or classroom assistants on low-compute setups.
Reasoning Tasks Performs well on logic puzzles, multi-step reasoning, and chain-of-thought queries.
Prototype Agents & Microservices Can be deployed in memory-efficient environments or as modular AI components.

Limitations

Limited Knowledge Capacity Due to small parameter size, lacks the depth and breadth of large-scale models.
Short-Term Context Handling Performs best with short to moderate-length prompts; lacks extended context support.
Creative Generation Limitations Output may lack diversity or depth in open-ended storytelling or imaginative tasks.
Token Budget Smaller output range; optimized for shorter and structured completions.
Basic Multilingual Support Some support for multilingual input, but less accurate than larger multilingual models.