💥 QwenGLM3.5-0.8B-GGUF

• 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)

• 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)

• 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

• 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.