---
license: mit
base_model: NovatasticRoScript/Atomight-2-1.5B-Thinking
tags:
- text-generation-inference
- transformers
- unsloth
- reasoning
- thought
- core-math
- instruction-tuning
model_creator: NovatasticRoScript
model_type: causal-lm
language:
- en
pipeline_tag: text-generation
---

<div align="center">

# ⚛️ Atomight-2-1.5B-Thinking

**A Deep-Reasoning Small Language Model Optimized for Sequential Logic Chains**

</div>

## 📌 Model Overview
**Atomight-2-1.5B-Thinking** is a specialized, compact reasoning model built on top of a 1.5B parameter core architecture. Engineered explicitly for users operating on constrained hardware environments (such as a free Google Colab T4 instance), Atomight-2 utilizes an explicit internal `<think>...</think>` scratchpad layout. It dynamically breaks down complex mathematical, logical, and structural prompts before committing to a final conclusion.

### 🚀 Key Highlights
* **Hardware Democratic:** High-tier deep reasoning accessible on consumer-grade hardware and free cloud compute tiers.
* **Structured Scratchpad:** Generates native, visible reasoning pathways natively formatted for transparent auditing.
* **Chat-Template Native:** Tailored directly for ChatML system configurations.

---

## 📊 Evaluation & Benchmark Results

Atomight-2 was subjected to a high-volume statistical evaluation matrix across core logic paradigms, matching up against premier industry baselines in the 1B–4B small language model class. 

### Official Performance Breakdown
The model displays exceptional specialization spikes in structured mathematical deduction, rivaling or outperforming significantly larger parameters classes on core numerical strings.

<div align="center">
  <img src="https://huggingface.co/NovatasticRoScript/Atomight-2-1.5B-Thinking/resolve/main/Note%20Original%20benchmarking%20of%20Atomight-2-1.5B-Thinking%20consists%20of.png" alt="Atomight-2 Official Benchmark Result" width="85%">
</div>

| Benchmark | Paradigm | Atomight-2-1.5B-Thinking | Qwen-2-1.5B-Instruct | Phi-3-mini (3.8B) | Llama-3.2-3B-Instruct |
| :--- | :--- | :---: | :---: | :---: | :---: |
| **GSM8k** | Math Logical Chains | **80.1%** | 71.0% | 82.5% | 73.1% |
| **ARC-C** | Core Reasoning | **88.5%** | 82.3% | 84.9% | 83.3% |
| **MMLU** | General Knowledge | **63.2%** | 56.7% | 68.8% | 61.1% |

> ⚠️ **Evaluation Insight:** While Atomight-2 exhibits class-leading spikes on core textual logic and mathematical proofs, it experiences a classic reasoning tradeoff. On abstract matrix-grid visual transformation evaluations (like ARC-AGI 2), it drops to a baseline floor of **0.00%**. This cognitive bottleneck highlights an instruction deficit in translating spatial imagery into basic structural text tokens—a major priority slated for the next architecture generation.

---

## 💻 Quickstart & Inference Code

To deploy Atomight-2 cleanly without encountering text-truncation errors inside the internal reasoning blocks, execute the generation using the official structured chat template format.

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

MODEL_ID = "NovatasticRoScript/Atomight-2-1.5B-Thinking"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID, 
    torch_dtype=torch.float16, 
    device_map="auto",
    trust_remote_code=True
)

# Structure conversational dialog into ChatML framework
messages = [
    {"role": "user", "content": "A retailer buys shirts for $15 and sells them for $25. What is the total profit on 12 shirts?"}
]

templated_input = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(templated_input, return_tensors="pt").to("cuda")

print("🧠 Generating Reasoning Sequence:")
outputs = model.generate(
    **inputs, 
    max_new_tokens=768, # Plentiful headroom required for deep-thinking scratchpads
    temperature=0.1,
    do_sample=False,
    pad_token_id=tokenizer.eos_token_id
)

print(tokenizer.decode(outputs[0], skip_special_tokens=False))