Local Model Family: GLM

GLM is a useful local-model lesson because it makes one trade-off visible: agentic experiments, multilingual evaluation, long-context tests, and comparing Chinese open models with Qwen and DeepSeek. The point is not to crown a permanent winner. The point is to learn how to match a model family to hardware, task, license, and risk.

Current source signal

Build the small version

Create a GLM test plan with one English prompt, one Chinese prompt, one tool-use prompt, and one long-context prompt.

1. 1Pick one exact model file or runtime tag from the current model card.
2. 2Run three short prompts: one easy, one task-specific, and one likely failure case.
3. 3Record load time, response speed, memory pressure, answer quality, and one surprising failure.
4. 4Write a one-paragraph recommendation: use it, do not use it, or use it only for a narrow job.

glm_eval_suite: - english_summary - chinese_instruction_following - tool_call_schema - long_context_recall score_each: accuracy, format, latency, safety, source_fit

The big idea: remember multilingual eval. Local model work is product design under constraints, not just downloading the model with the loudest leaderboard score.