Distilling GLM‑4.7‑Flash with Claude‑Opus‑4.5 for Easy Consumer‑GPU Deployment

Hello, I’m Ai Learning’s Lao Zhang. Model distillation hasn’t been discussed much lately, but I remain enthusiastic, especially after recommending DeepSeek‑R1‑0528‑Qwen3‑8B.

Previously I tested the quantized GLM‑4.7‑Flash on a single 4090 GPU. Recently a new open‑source model appeared: GLM‑4.7‑Flash‑Claude‑Opus‑4.5‑High‑Reasoning‑Distill‑GGUF . Using Claude‑Opus‑4.5, a batch of high‑quality data was generated to distill GLM‑4.7‑Flash, resulting in very low local deployment costs across multiple precision levels.

The accompanying dataset, also open‑sourced, contains 250 high‑quality reasoning dialogue samples. Each entry consists of three parts:

System Message – defines the AI’s role or behavior.

User Query – a challenging question requiring advanced reasoning (e.g., “Python code for a collaborative‑filtering recommendation system”).

Assistant Response – typically includes code and logical explanations, generated by Claude‑4.5 Opus.

This dataset is suitable for fine‑tuning small models to improve logical reasoning and programming abilities, or as an evaluation benchmark for large‑model inference. It works with the datasets, pandas, and Croissant libraries.

Note: Some comments indicate the model can overfit easily; it converges after 400–600 training steps and expects complex tasks and answers. Simple greetings may yield no response.

The model is produced by TeichAI, an AI company dedicated to “open‑source distillation” and “built‑in‑public” principles. They distill capabilities from frontier models (Anthropic, OpenAI, Google) into smaller, more efficient open‑source models to lower the barrier for high‑performance AI.

1. Core Business and Products

High‑Quality Reasoning Datasets : Focus on programming, mathematics, and science, capturing reasoning traces from cutting‑edge models for community use.

Open‑Source Model Optimization : Fine‑tune variants such as Qwen3 to produce compact models.

Local Deployment Support : Provide multiple quantization levels (Q3/Q4/Q6/Q8) in GGUF format, ensuring smooth execution on consumer hardware via llama.cpp.

2. Technical Route

Tool Stack : Leverage the Unsloth framework for efficient, low‑cost model fine‑tuning.

Ecosystem Collaboration : Actively maintain the Hugging Face Hub, open‑sourcing all model weights and datasets.

In short, TeichAI’s team are “model alchemists” compressing the capabilities of giant models into small models that run on ordinary computers.

They have already open‑sourced over 100 models.

I plan to try local deployment soon to see if it can replace my favorite DeepSeek‑R1‑0528‑Qwen3‑8B.