---
license: apache-2.0
language:
- en
base_model: Qwen/Qwen3.5-0.8B-Base
pipeline_tag: text-generation
tags:
- reasoning
- cot
- math
- gpqa
- gsm8k
- quasar-1
---
![Quasar Vid](uasar_20260610_172613_0000.gif)

# 🌌 Quasar-1-0.8B

**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.

## Chat with the model:

here is the 🤗 space to chat with the model [Ion-Chat](https://huggingface.co/spaces/Axiom-AI/Ion-Chat)

## 📌 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](https://huggingface.co) |
| **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](https://github.com). 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](https://huggingface.co), 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**.