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Model: ArcOffical/PiCo-1B Source: Original Platform
2026-07-20 16:38:09 +08:00
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+---
+license: openrail
+language:
+- en
+pipeline_tag: text-generation
+---
+# 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.
+
+---
+
+## 📌 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.
+
+---
+
+## 📋 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 |
+
+---
+
+## 📊 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.
+
+![mmlu_comparison](https://cdn-uploads.huggingface.co/production/uploads/67f51c8931456897b8918a16/xRmbn3pMYD6hLVEPZYwBa.png)
+
+---
+
+### GSM8K (Grade School Math)
+
+Measures mathematical reasoning with grade-school level word problems.
+
+![gsm8k_comparison](https://cdn-uploads.huggingface.co/production/uploads/67f51c8931456897b8918a16/E03-rJ-NpEwCH5M97ec-v.png)
+
+---
+
+### ARC-Challenge (AI2 Reasoning Challenge)
+
+Measures science reasoning with grade-level science questions (harder subset).
+
+![arc_challenge_comparison](https://cdn-uploads.huggingface.co/production/uploads/67f51c8931456897b8918a16/WOOinEwZ0DaRKJl74n6E9.png)
+
+---
+
+### ARC-Easy (AI2 Reasoning Challenge)
+
+Measures basic science reasoning with grade-level science questions (easier subset).
+
+![arc_easy_comparison](https://cdn-uploads.huggingface.co/production/uploads/67f51c8931456897b8918a16/BTGhsnWy0gA6iJaHBr8j4.png)
+
+---
+
+### HellaSwag (Commonsense Reasoning)
+
+Measures commonsense natural language inference with everyday scenarios.
+
+![hellaswag_comparison](https://cdn-uploads.huggingface.co/production/uploads/67f51c8931456897b8918a16/pNLjRk1Xy6aoGFOik3Zsv.png)
+
+---
+
+### HumanEval (Code Generation)
+
+Measures functional correctness of code generation across 164 programming problems.
+
+![humaneval_comparison](https://cdn-uploads.huggingface.co/production/uploads/67f51c8931456897b8918a16/FnhNO9sBCDl-Ky438q6ll.png)
+
+---
+
+### TruthfulQA (Truthfulness)
+
+Measures whether the model generates truthful answers rather than mimicking common misconceptions.
+
+![truthfulqa_comparison](https://cdn-uploads.huggingface.co/production/uploads/67f51c8931456897b8918a16/pKq1fhlzmfistFhZ8BRRt.png)
+
+---
+
+## 🏆 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
+
+---
+
+## 🚀 Usage
+
+### Quick Start
+
+```python
+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
+
+---
+
+## 🏋️ 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 |
+
+---
+
+## ⚠️ 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
+
+---
+
+## 📝 Citation
+
+If you use PiCo 1B in your research or projects, please cite:
+
+```bibtex
+@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}},
+}
+```
+
+---
+
+## 📄 License
+
+This model is released under an open-source license. Please see the LICENSE file for details.
+
+---
+
+*Last updated: June 2026*