💥 QwenGLM3.5-0.8B-GGUF

📄 Overview

Base Model	constructai/QwenGLM3.5-0.8B
Parameters	0.9B

Quant types

Quant type	Size
Q2_K	422 MB
Q3_K_S	435 MB
Q3_K_M	466 MB
Q3_K_L	491 MB
IQ4_XS	506 MB
Q4_K_S	505 MB
Q4_K_M	529 MB
Q5_K_S	564 MB
Q5_K_M	578 MB
Q6_K	630 MB
Q8_0	812 MB
F16	1.52 GB

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🎯 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.

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⚠️ 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

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🙏 Acknowledgements

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

• 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 (Alibaba Cloud) — for releasing the Qwen3.5-0.8B-Base model under Apache 2.0, a perfect balance of size and intelligence.

• Unsloth AI — for making fine‑tuning on consumer hardware fast and memory‑efficient.

• Hugging Face — for the ecosystem (transformers, datasets, PEFT, Hub) that democratises LLM training.

• Kaggle — for providing free T4 GPU runtime to run this experiment.

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📖 Citation

@misc{QwenGLM3.5-0.8B-GGUF,
  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-GGUF},
}