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Model: NoesisLab/Arcade-3B Source: Original Platform
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---
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language:
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- en
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license: apache-2.0
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base_model: HuggingFaceTB/SmolLM3-3B
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tags:
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- smollm
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- smolreasoner
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- reasoning
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- instruction-tuned
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- arcade
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- sc-orthogonal
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pipeline_tag: text-generation
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---
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# Arcade-3B — SmolReasoner
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[](https://doi.org/10.5281/zenodo.19029063)[](https://opensource.org/licenses/Apache-2.0)
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[](https://huggingface.co/HuggingFaceTB/SmolLM3-3B)
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[](https://huggingface.co/NoesisLab)
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[](https://huggingface.co/NoesisLab/Arcade-3B)
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[](https://huggingface.co/NoesisLab/Arcade-3B)
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**Arcade-3B** is a 3B instruction-following and reasoning model built on [SmolLM3-3B](https://huggingface.co/HuggingFaceTB/SmolLM3-3B).
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It is the first public release from the **ARCADE** project at [NoesisLab](https://huggingface.co/NoesisLab), which investigates the *State–Constraint Orthogonality Hypothesis*: standard Transformer hidden states conflate factual content and reasoning structure in the same subspace, and explicitly decoupling them improves generalization.
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---
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## Method: SC-Orthogonal Training
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Standard Transformer hidden states conflate two distinct functions:
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| Half | Symbol | Role |
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|------|--------|------|
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| `H[..., :D/2]` | **S** (State) | *What* the model knows — factual content |
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| `H[..., D/2:]` | **C** (Constraint) | *How* to retrieve it — reasoning structure |
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ARCADE's **SCOrthoTrainer** injects an orthogonality penalty on the final hidden layer, encouraging S and C to decouple in representation space without modifying any attention operators:
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$$\mathcal{L}_{\text{total}} = \mathcal{L}_{\text{CE}} + \frac{\lambda}{B \cdot L} \sum_{b,l} \left( \mathbf{S}_{b,l} \cdot \mathbf{C}_{b,l} \right)^2$$
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with **λ = 0.1**. This soft regularization reduces divergence errors at inference time at zero architectural cost.
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---
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## Training Details
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| Setting | Value |
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|---------|-------|
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| Base model | `HuggingFaceTB/SmolLM3-3B` |
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| λ (orth penalty) | 0.1 |
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| Max sequence length | 2048 |
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| Learning rate | 2e-4 (cosine) |
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| Steps | 10 000 |
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| Effective batch | 16 sequences/step |
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| Hardware | 1 × A100-80 GB |
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| Precision | bfloat16 |
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### Training Data
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| Dataset | Split | Sampling weight |
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|---------|-------|-----------------|
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| [nohurry/Opus-4.6-Reasoning-3000x-filtered](https://huggingface.co/datasets/nohurry/Opus-4.6-Reasoning-3000x-filtered) | train (2.3 K) | 10 % |
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| [HuggingFaceTB/smol-smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk) | train (460 K) | 45 % |
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| [OpenDataArena/ODA-Mixture-500k](https://huggingface.co/datasets/OpenDataArena/ODA-Mixture-500k) | train (500 K) | 45 % |
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Reasoning samples are wrapped with `<think>…</think>` tags and upsampled 10× to compensate for the small dataset size.
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---
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## Evaluation
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Results from [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness):
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### Comparison with Peer Models
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> `< 10%` entries are displayed as `<10%` in the chart.
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| Benchmark | Arcade-3B | Gemma-2-2B | Llama-2-7B | Qwen1.5-1.8B | OpenLLaMA-v2-3B |
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|-----------|-----------|------------|------------|--------------|-----------------|
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| MMLU | **52.9%** | 52.4% | 45.3% | 46.8% | 41.0% |
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| GSM8K | **62.9%** | 50.9% | 14.6% | 37.8% | < 10% |
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| HumanEval | **41.5%** | 32.3% | 12.8% | 27.4% | < 10% |
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| ARC-Challenge | 52.6% | **53.1%** | 46.2% | 41.2% | 34.2% |
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| ARC-Easy | 74.4% | **75.9%** | 75.3% | 66.8% | 68.1% |
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### Arcade-3B Detailed Scores
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| Benchmark | Few-shot | Metric | Score | ± |
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|-----------|----------|--------|-------|---|
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| GSM8K | 5 | flexible-extract / exact_match | **0.6293** | 0.0133 |
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| HumanEval | 0 | pass@1 | **0.4146** | 0.0386 |
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| ARC-Challenge | 25 | acc_norm | **0.5256** | 0.0146 |
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| ARC-Easy | 0 | acc | **0.7437** | 0.0090 |
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| MMLU | 0 | acc | **0.5293** | 0.0040 |
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---
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## Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "NoesisLab/Arcade-3B"
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tok = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [{"role": "user", "content": "Solve step by step: If a train travels 120 km in 1.5 hours, what is its average speed?"}]
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input_ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
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output = model.generate(input_ids, max_new_tokens=512, temperature=0.7, do_sample=True)
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print(tok.decode(output[0][input_ids.shape[-1]:], skip_special_tokens=True))
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```
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For step-by-step reasoning, the model may emit a `<think>…</think>` block before the final answer.
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---
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## Citation
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```bibtex
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@misc{noesislab2025arcade,
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title = {ARCADE: State-Constraint Orthogonal Training},
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author = {NoesisLab},
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year = {2025},
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howpublished = {\url{https://huggingface.co/NoesisLab/Arcade-3B}},
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}
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```
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---
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## License
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Apache 2.0 — inherited from SmolLM3-3B.
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benchmark_comparison.png
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size 127895
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chat_template.jinja
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{# ───── defaults ───── #}
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{%- if enable_thinking is not defined -%}
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{%- set enable_thinking = true -%}
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{%- endif -%}
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{# ───── reasoning mode ───── #}
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{%- if enable_thinking -%}
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{%- set reasoning_mode = "/think" -%}
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{%- else -%}
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{%- set reasoning_mode = "/no_think" -%}
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{%- endif -%}
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{# ───── header (system message) ───── #}
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{{- "<|im_start|>system\n" -}}
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{%- if messages[0].role == "system" -%}
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{%- set system_message = messages[0].content -%}
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{%- if "/no_think" in system_message -%}
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{%- set reasoning_mode = "/no_think" -%}
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{%- elif "/think" in system_message -%}
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{%- set reasoning_mode = "/think" -%}
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{%- endif -%}
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{%- set custom_instructions = system_message.replace("/no_think", "").replace("/think", "").rstrip() -%}
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{%- endif -%}
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{%- if "/system_override" in system_message -%}
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{{- custom_instructions.replace("/system_override", "").rstrip() -}}
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{{- "<|im_end|>\n" -}}
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{%- else -%}
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{{- "## Metadata\n\n" -}}
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{{- "Knowledge Cutoff Date: June 2025\n" -}}
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{%- set today = strftime_now("%d %B %Y") -%}
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{{- "Today Date: " ~ today ~ "\n" -}}
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{{- "Reasoning Mode: " + reasoning_mode + "\n\n" -}}
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{{- "## Custom Instructions\n\n" -}}
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{%- if custom_instructions -%}
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{{- custom_instructions + "\n\n" -}}
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{%- elif reasoning_mode == "/think" -%}
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{{- "You are a helpful AI assistant named Arcade, trained by Hugging Face. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracking, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> Thought section </think> Solution section. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion.\n\n" -}}
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{%- else -%}
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{{- "You are a helpful AI assistant named Arcade, trained by Hugging Face.\n\n" -}}
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{%- endif -%}
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{%- if xml_tools or python_tools or tools -%}
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{{- "### Tools\n\n" -}}
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{%- if xml_tools or tools -%}
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{%- if tools -%}
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{%- set xml_tools = tools -%}
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{%- endif -%}
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{%- set ns = namespace(xml_tool_string="You may call one or more functions to assist with the user query.\nYou are provided with function signatures within <tools></tools> XML tags:\n\n<tools>\n") -%}
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{%- for tool in xml_tools[:] -%} {# The slicing makes sure that xml_tools is a list #}
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{%- set ns.xml_tool_string = ns.xml_tool_string ~ (tool | string) ~ "\n" -%}
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{%- endfor -%}
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{%- set xml_tool_string = ns.xml_tool_string + "</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>" -%}
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{{- xml_tool_string -}}
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{%- endif -%}
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{%- if python_tools -%}
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{%- set ns = namespace(python_tool_string="When you send a message containing Python code between '<code>' and '</code>' tags, it will be executed in a stateful Jupyter notebook environment, and you will then be given the output to continued reasoning in an agentic loop.\n\nYou can use the following tools in your python code like regular functions:\n<tools>\n") -%}
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{%- for tool in python_tools[:] -%} {# The slicing makes sure that python_tools is a list #}
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{%- set ns.python_tool_string = ns.python_tool_string ~ (tool | string) ~ "\n" -%}
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{%- endfor -%}
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{%- set python_tool_string = ns.python_tool_string + "</tools>\n\nThe state persists between code executions: so variables that you define in one step are still available thereafter." -%}
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{{- python_tool_string -}}
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{%- endif -%}
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{{- "\n\n" -}}
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{{- "<|im_end|>\n" -}}
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{%- endif -%}
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{%- endif -%}
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{# ───── main loop ───── #}
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{%- for message in messages -%}
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{%- set content = message.content if message.content is string else "" -%}
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{%- if message.role == "user" -%}
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{{ "<|im_start|>" + message.role + "\n" + content + "<|im_end|>\n" }}
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{%- elif message.role == "assistant" -%}
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{% generation %}
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{%- if reasoning_mode == "/think" -%}
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{{ "<|im_start|>assistant\n" + content.lstrip("\n") + "<|im_end|>\n" }}
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{%- else -%}
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{{ "<|im_start|>assistant\n" + "<think>\n\n</think>\n" + content.lstrip("\n") + "<|im_end|>\n" }}
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{%- endif -%}
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{% endgeneration %}
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{%- elif message.role == "tool" -%}
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{{ "<|im_start|>" + "user\n" + content + "<|im_end|>\n" }}
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{%- endif -%}
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{%- endfor -%}
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{# ───── generation prompt ───── #}
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{%- if add_generation_prompt -%}
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{%- if reasoning_mode == "/think" -%}
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{{ "<|im_start|>assistant\n" }}
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{%- else -%}
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{{ "<|im_start|>assistant\n" + "<think>\n\n</think>\n" }}
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{%- endif -%}
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{%- endif -%}
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config.json
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config.json
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{
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"architectures": [
|
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"SmolLM3ForCausalLM"
|
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],
|
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"attention_bias": false,
|
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"attention_dropout": 0.0,
|
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"bos_token_id": null,
|
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"dtype": "bfloat16",
|
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"eos_token_id": 128012,
|
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"hidden_act": "silu",
|
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"hidden_size": 2048,
|
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"initializer_range": 0.02,
|
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"intermediate_size": 11008,
|
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"layer_types": [
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention"
|
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],
|
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"max_position_embeddings": 65536,
|
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"max_window_layers": 28,
|
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"mlp_bias": false,
|
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"model_type": "smollm3",
|
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"no_rope_layer_interval": 4,
|
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"no_rope_layers": [
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"num_attention_heads": 16,
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||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 4,
|
||||
"pad_token_id": 128012,
|
||||
"pretraining_tp": 1,
|
||||
"rms_norm_eps": 1e-06,
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||||
"rope_parameters": {
|
||||
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"rope_type": "default"
|
||||
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|
||||
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||||
"tie_word_embeddings": true,
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||||
"transformers_version": "5.3.0",
|
||||
"use_cache": false,
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"use_sliding_window": false,
|
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"vocab_size": 128256
|
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}
|
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dia.jpg
Normal file
3
dia.jpg
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:c4008b2bfd2fbba7cd0600f3ddd7ab064ed7dffc8c4e856a5f888adf25b9dabd
|
||||
size 207006
|
||||
10
generation_config.json
Normal file
10
generation_config.json
Normal file
@@ -0,0 +1,10 @@
|
||||
{
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
128012
|
||||
],
|
||||
"pad_token_id": 128012,
|
||||
"temperature": 0.6,
|
||||
"top_p": 0.95,
|
||||
"transformers_version": "5.3.0"
|
||||
}
|
||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:ca13b22db96e7ec59e8f2c86a5a725c436e7663c1a8f294b1e884211f13a492d
|
||||
size 6150235096
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:7b6a500b662a34eb3f0374db856ba4ad7de4c81040571d78dc0d357238930005
|
||||
size 17208819
|
||||
14
tokenizer_config.json
Normal file
14
tokenizer_config.json
Normal file
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"backend": "tokenizers",
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": true,
|
||||
"eos_token": "<|im_end|>",
|
||||
"fast": false,
|
||||
"is_local": false,
|
||||
"model_input_names": [
|
||||
"input_ids",
|
||||
"attention_mask"
|
||||
],
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|im_end|>"
|
||||
}
|
||||
Reference in New Issue
Block a user