ArcOffical/PiCo-1B

PiCo 1B

A 1B-parameter dense language model optimized for reasoning and knowledge tasks.

For clarity, our model uses the tokenizer from Qwen 2 1.5B but has been trained from scratch — it is not a fine-tuned version of Qwen 2 1.5B.

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📌 Model Overview

PiCo 1B is a compact, high-performance language model with ~1.46 billion parameters. Despite its small size, it achieves competitive performance across reasoning, knowledge, and coding benchmarks, particularly excelling in science reasoning tasks.

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📋 Model Details

Attribute	Value
Model Size	~1.46B parameters
Architecture	Dense transformer (decoder-only)
Context Length	2048 tokens
Precision	FP32 / FP16 / Safetensors
License	Open-source

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📊 Benchmark Results

PiCo 1B is evaluated against 31 open-source models in the 1B–2B parameter range across 7 standard benchmarks.

MMLU (Massive Multitask Language Understanding)

Measures general knowledge across 57 subjects including STEM, humanities, and social sciences.

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GSM8K (Grade School Math)

Measures mathematical reasoning with grade-school level word problems.

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ARC-Challenge (AI2 Reasoning Challenge)

Measures science reasoning with grade-level science questions (harder subset).

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ARC-Easy (AI2 Reasoning Challenge)

Measures basic science reasoning with grade-level science questions (easier subset).

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HellaSwag (Commonsense Reasoning)

Measures commonsense natural language inference with everyday scenarios.

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HumanEval (Code Generation)

Measures functional correctness of code generation across 164 programming problems.

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TruthfulQA (Truthfulness)

Measures whether the model generates truthful answers rather than mimicking common misconceptions.

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🏆 Performance Highlights

✅ Strengths

• Science Reasoning: Best-in-class performance on ARC-Easy and ARC-Challenge
• General Knowledge: Top 3 on MMLU, outperforming many larger 1.5B–2B models
• Coding Ability: Strong HumanEval performance, competitive with models 2x its size
• Truthfulness: Top 5 on TruthfulQA, demonstrating reliable factual output

📈 Areas for Improvement

• Commonsense Reasoning: HellaSwag score lags behind modern 1.5B+ models
• Mathematical Reasoning: GSM8K performance is solid but not top-tier
• Scale: Further training on larger, more diverse datasets could boost all benchmarks

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🚀 Usage

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "pico-1b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Explain the theory of relativity in simple terms."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Model Formats

• Safetensors (recommended): Secure and fast loading
• PyTorch (FP16): Standard format
• GGUF: For local inference with llama.cpp

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🏋️ Training Details

Aspect	Description
Architecture	Dense decoder-only transformer
Optimizer	AdamW
Learning Rate	Cosine schedule with warmup
Batch Size	Configurable per GPU setup
Training Framework	PyTorch + Hugging Face Transformers

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⚠️ Limitations

• Small Model Size: As a 1B-parameter model, it has inherent limitations compared to larger models (7B+) on complex reasoning tasks
• Training Data: Primarily trained on English text; performance on non-English languages may be limited
• Hallucinations: Like all LLMs, it may generate factually incorrect information
• Context Window: Limited to 2048 tokens by default

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📝 Citation

If you use PiCo 1B in your research or projects, please cite:

@misc{pico1b,
  title={PiCo 1B: A Compact Language Model Optimized for Reasoning},
  author={Arc Develop Team},
  year={2026},
  howpublished={\url{https://github.com/pico-llm/pico-1b}},
}

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📄 License

This model is released under an open-source license. Please see the LICENSE file for details.

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Last updated: June 2026