94 lines
4.1 KiB
Markdown
94 lines
4.1 KiB
Markdown
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---
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license: mit
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base_model: NovatasticRoScript/Atomight-2-1.5B-Thinking
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- reasoning
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- thought
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- core-math
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- instruction-tuning
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model_creator: NovatasticRoScript
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model_type: causal-lm
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language:
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- en
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pipeline_tag: text-generation
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new_version: NovatasticRoScript/Atomight-V2.1-0.5B-Inference
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---
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<div align="center">
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# ⚛️ Atomight-2-1.5B-Thinking
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**A Deep-Reasoning Small Language Model Optimized for Sequential Logic Chains**
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</div>
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## 📌 Model Overview
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**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.
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### 🚀 Key Highlights
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* **Hardware Democratic:** High-tier deep reasoning accessible on consumer-grade hardware and free cloud compute tiers.
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* **Structured Scratchpad:** Generates native, visible reasoning pathways natively formatted for transparent auditing.
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* **Chat-Template Native:** Tailored directly for ChatML system configurations.
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---
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## 📊 Evaluation & Benchmark Results
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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.
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### Official Performance Breakdown
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The model displays exceptional specialization spikes in structured mathematical deduction, rivaling or outperforming significantly larger parameters classes on core numerical strings.
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<div align="center">
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<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%">
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</div>
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| Benchmark | Paradigm | Atomight-2-1.5B-Thinking | Qwen-2-1.5B-Instruct | Phi-3-mini (3.8B) | Llama-3.2-3B-Instruct |
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| :--- | :--- | :---: | :---: | :---: | :---: |
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| **GSM8k** | Math Logical Chains | **80.1%** | 71.0% | 82.5% | 73.1% |
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| **ARC-C** | Core Reasoning | **88.5%** | 82.3% | 84.9% | 83.3% |
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| **MMLU** | General Knowledge | **63.2%** | 56.7% | 68.8% | 61.1% |
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> ⚠️ **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.
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---
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## 💻 Quickstart & Inference Code
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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.
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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MODEL_ID = "NovatasticRoScript/Atomight-2-1.5B-Thinking"
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID,
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torch_dtype=torch.float16,
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device_map="auto",
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trust_remote_code=True
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)
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# Structure conversational dialog into ChatML framework
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messages = [
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{"role": "user", "content": "A retailer buys shirts for $15 and sells them for $25. What is the total profit on 12 shirts?"}
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]
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templated_input = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(templated_input, return_tensors="pt").to("cuda")
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print("🧠 Generating Reasoning Sequence:")
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outputs = model.generate(
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**inputs,
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max_new_tokens=768, # Plentiful headroom required for deep-thinking scratchpads
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temperature=0.1,
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do_sample=False,
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pad_token_id=tokenizer.eos_token_id
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=False))
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