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.

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Key Highlights

1. Compact & Efficient Lightweight architecture (135M) suitable for fast inference, mobile applications, and edge deployment.

2. Reasoning-Centric Training Fine-tuned on high-quality reasoning and instruction datasets like Celestia3-DeepSeek-R1-0528, focusing on multi-step logical thinking.

3. Low-Resource Optimization Designed to run effectively on CPUs or single-GPU setups with minimal memory footprint.

4. Structured Outputs Supports generation of clean, structured content including lists, steps, tables, and JSON-like responses.

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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]))

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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.

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Limitations

1. Limited Knowledge Capacity Due to small parameter size, lacks the depth and breadth of large-scale models.

2. Short-Term Context Handling Performs best with short to moderate-length prompts; lacks extended context support.

3. Creative Generation Limitations Output may lack diversity or depth in open-ended storytelling or imaginative tasks.

4. Token Budget Smaller output range; optimized for shorter and structured completions.

5. Basic Multilingual Support Some support for multilingual input, but less accurate than larger multilingual models.