Quasar-1-0.8B is a compact reasoning-oriented language model built upon Qwen/Qwen3.5-0.8B-Base. The model is designed to maximize multi-step mathematical reasoning and stable zero-shot execution while remaining lightweight enough for local inference on highly constrained hardware.

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📌 Highlights

🚀 Sub-1B parameter model
🧮 Strong zero-shot mathematical reasoning
📉 Efficient local execution
🔍 Fully reproducible evaluation artifacts
📂 Raw benchmark logs included
⚖️ Apache-2.0 licensed

📊 Model Overview

Property	Value
Base Model	Qwen/Qwen3.5-0.8B-Base
Parameters	~0.8B
Architecture	Transformer
License	Apache-2.0
Intended Use	Reasoning, mathematics, local inference
Precision Tested	`float16`

📈 Evaluation

All results below correspond to clean baseline runs performed with lm-evaluation-harness. No scores were manually edited or cherry-picked.

Zero-Shot Performance

Model	GPQA (0-shot)	GSM8K (0-shot)
Quasar-1-0.8B	19.7%	39.8%
OrionLLM/GRM-2.5-Air	12.5%	—
Qwen/Qwen3.5-0.8B-Base	11.9%	~15–20%
Random Guessing Baseline	25.0%	~0%

🔍 Observations

Stable Zero-Shot Execution: Quasar-1-0.8B consistently produces structured outputs under default evaluation settings, allowing direct usage with standard lm-evaluation-harness configurations.
Mathematical Capability: Despite operating at sub-1B scale, the model demonstrates strong performance on GSM8K, suggesting useful multi-step arithmetic and reasoning capabilities.
Edge Efficiency: The model is intended for local execution on low-resource hardware, enabling experimentation without requiring modern accelerators.

💻 Hardware Used

The reported results were obtained on standard consumer hardware:

Component	Specification
OS	Windows 10 Pro 64-bit
CPU	Intel Pentium G4560 @ 3.50 GHz
GPU	NVIDIA GTX 1050 Ti
Framework	PyTorch 2.6.0
CUDA	12.4
Transformers	5.10.2
lm-eval	0.4.13.dev0
Precision	`float16`

📂 Reproducibility Artifacts

Included evaluation files:

gpqa_results_2026-06-05T17-38-23.426157.json
results_2026-06-09T15-53-28.196995.json

These files contain:

Runtime environment information
Framework versions
Evaluation settings
Benchmark metrics
Full telemetry metadata

🎯 Intended Uses

Suitable for:

Mathematical reasoning
Educational experiments
Lightweight local inference
Edge devices
Research on small language models

🛑 Limitations

Quasar-1-0.8B remains a compact model and should not be expected to match larger frontier systems on broad knowledge or complex long-horizon reasoning tasks. Benchmark scores represent only the evaluated tasks and should not be interpreted as comprehensive measurements of intelligence or general capability.

📜 Citation

bibtex

@misc{quasar1,
  title={Quasar-1-0.8B},
  year={2026},
  license={Apache-2.0},
  base_model={Qwen/Qwen3.5-0.8B-Base}
}

⚖️ License

Released under the Apache-2.0 License.

Axiom-AI/Quasar-1-0.8B

🌌 Quasar-1-0.8B