Open folder
Open your project folder in the agent chat.
Workflows
machine learning model compression workflow
03
Estimate weight size before download
Before downloading a large model, you can estimate its size on disk. This helps you check free space and plan your storage needs.
Write script
Create a Python script to calculate weight sizes.
Run command
Execute the script in the terminal window.
Check output
Review the printed size estimates for each bit depth.
Example script
Write a script to estimate model weight size.
- write_file
size_estimate.pysaved - Shell
python size_estimate.pyexit 0 - read_file
size_estimate.py18 lines - Shell
python size_estimate.pyexit 0
waiting for your OK before the next stepRejectAccept
size_estimate.py
# comment
params = 7000000000
for b in [32, 16, 8, 4]:
size_gb = (params * b) / 1e9
print(f"{b}-bit: {size_gb:.1f} GB") Code ready.This script estimates the weight size only for a 7 billion parameter model at different bit depths without network access.
model compression deep learning steps
Write conversion script
The agent uses write_file to create your conversion script and shows a diff so you can review the changes before approving in Ask first mode. Approve it. Shell runs your conversion script and returns the real output. The terminal displays the exit code and timing details clearly. The difference between 4bit quantization methods affects model quality and speed. list_dir shows the new file size for comparison purposes today. read_file checks the logs for any warnings or errors found. machine learning model compression confirms the workflow steps effectively now.
Run evaluation script
The agent writes your evaluation script using edit_file and shows a diff for your approval before you review the metrics to ensure that the testing is correct. You approve. The agent runs the script via Shell and reports the exit code. Perplexity and accuracy scores appear when your evaluation script prints them. Quantization tradeoffs in llm inference affect speed and quality, so you measure tokens per second. The agent uses grep to search the output for key scores. You compare these results with your baseline to decide if the quality loss is acceptable. This model compression deep learning step helps you generate my own dynamic quant.
