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---
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
new_version: NovatasticRoScript/Atomight-V2.1-0.5B-Inference
---
<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))

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %} {{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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{
"architectures": [
"Qwen2ForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": null,
"torch_dtype": "float16",
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 1536,
"initializer_range": 0.02,
"intermediate_size": 8960,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
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"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 32768,
"max_window_layers": 21,
"model_type": "qwen2",
"num_attention_heads": 12,
"num_hidden_layers": 28,
"num_key_value_heads": 2,
"pad_token_id": 151665,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"rope_theta": 1000000.0,
"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": true,
"unsloth_fixed": true,
"unsloth_version": "2026.5.5",
"use_cache": false,
"use_sliding_window": false,
"vocab_size": 151936
}

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{
"do_sample": true,
"eos_token_id": [
151645,
151643
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"max_length": 32768,
"pad_token_id": 151665,
"repetition_penalty": 1.1,
"temperature": 0.7,
"top_k": 20,
"top_p": 0.8,
"transformers_version": "5.5.0"
}

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{
"add_prefix_space": false,
"backend": "tokenizers",
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"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"is_local": false,
"model_max_length": 32768,
"pad_token": "<|PAD_TOKEN|>",
"padding_side": "left",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null,
"added_tokens_decoder": {
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},
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %} {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
}