---
license: apache-2.0
pipeline_tag: text-generation
tags:
- unsloth
datasets:
- Jackrong/GLM-5.1-Reasoning-1M-Cleaned
---

# 💥 QwenGLM3.5-0.8B

**📄 Overview**

| | |
|---|---|
| **Model Name** | QwenGLM3.5-0.8B |
| **Base Model** | Qwen3.5-0.8B-Base |
| **Dataset** | Jackrong/GLM-5.1-Reasoning-1M-Cleaned (5,000 samples) |
| **Training Type** | Supervised Fine-Tuning (SFT) |
| **Parameters** | 0.9B |
| **Framework** | Unsloth + LoRA |
| **Hardware** | NVIDIA T4 16GB |

---

**🎯 Intended Use**

This model is designed for **step‑by‑step reasoning tasks** where the answer requires logical decomposition before the final response. It is optimized for:

- **Educational applications** — explaining "why" and "how" questions
- **On‑device assistants** — runs on mobile, Raspberry Pi, or CPU‑only environments
- **Fast prototyping** — small footprint (0.9B parameters), low latency
- **Reasoning distillation research** — studying how small models learn from large ones (GLM → Qwen)

**Not recommended for:** multimodal tasks, non‑reasoning chat (e.g., creative writing), or production systems requiring 100% factual accuracy.

---

**⚠️ Limitations & Intended Use**

Intended Use:

* Educational & Reasoning tasks — explaining step‑by‑step logic (math, science, common sense)

* On‑device assistants — runs on CPU, Raspberry Pi, mobile (small footprint, fast inference)

* Research baseline — for studying SFT‑only reasoning without RLHF/DPO

* Distillation experiments — testing how well small models learn from large (GLM → Qwen)

Limitations:

* Size matters — 0.9B parameters, so complex or multi‑hop reasoning may still fail

* No multimodal — text only; images, video, audio are not supported

* Factual accuracy — may hallucinate or give incorrect answers; always verify critical outputs

* Domain restricted — trained on 5,000 reasoning examples; general chat or creative writing may be suboptimal

* Training data bias — inherits biases from GLM-5.1-Reasoning dataset; not safety‑filtered for harmful content

* Hardware specific — optimised for T4/consumer GPUs; very slow on CPU without quantisation

---

**🙏 Acknowledgements**

This project would not have been possible without the open‑source community and the following resources:

* [Jackrong](https://huggingface.co/Jackrong) — for creating the GLM-5.1-Reasoning-1M-Cleaned dataset and sharing detailed fine‑tuning insights. His work inspired the QwenGLM concept.

* [Qwen Team](https://huggingface.co/Qwen) (Alibaba Cloud) — for releasing the Qwen3.5-0.8B-Base model under Apache 2.0, a perfect balance of size and intelligence.

* [Unsloth AI](https://huggingface.co/unsloth) — for making fine‑tuning on consumer hardware fast and memory‑efficient.

* [Hugging Face](https://huggingface.co/) — for the ecosystem (transformers, datasets, PEFT, Hub) that democratises LLM training.

* [Kaggle](https://www.kaggle.com) — for providing free T4 GPU runtime to run this experiment.

---

**📖 Citation**

```bibtex
@misc{QwenGLM3.5-0.8B,
  author = {constructai},
  title = {QwenGLM3.5-0.8B: Small Reasoning Model via SFT on GLM Traces},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {https://huggingface.co/constructai/QwenGLM3.5-0.8B},
}
```