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NovatasticRoScript
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Model Details
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NovatasticRoScript
Model Tree
NovatasticRoScript/Atomight-2-1.5B-Thinking
Input Modalities
Output Modalities
Supported Functionality
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.
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.
The model displays exceptional specialization spikes in structured mathematical deduction, rivaling or outperforming significantly larger parameters classes on core numerical strings.
| 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.
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 torchfrom 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 frameworkmessages = [ {"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))import torchfrom 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 frameworkmessages = [ {"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))