README.md

---
license: apache-2.0
datasets:
- sequelbox/Celestia3-DeepSeek-R1-0528
base_model:
- HuggingFaceTB/SmolLM2-135M-Instruct
language:
- en
pipeline_tag: text-generation
library_name: transformers
tags:
- trl
- text-generation-inference
- re-think
- reasoning
---
 
![sm2.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/jhbqEfst42Fa2oDNZwTyV.png)

# **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**

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.

---

## **Quickstart with 🤗 Transformers**

```python
%%capture
!pip install transformers
```

```python
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**

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.