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---
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
---
# **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. |