Second-Me/lpm_kernel
JimmyZQX f5bb0dad59
Data Filtering with Gemma (#396)
* Add code for data filtering llm judge

* Ignore log file created on root (mainly for synthetic_data_generation.log)

* Fix metadata API compatibility issues by commenting out metadata tags in LLM API calls

- Commented out metadata.tags parameters in all LLM API calls across the codebase
- This fixes compatibility issues with custom LLM providers that don't support metadata
- Affects shades generation, topics generation, wiki generation, bio QA, and question generation
- Preserves the original code structure for future re-enabling if needed

* feat: add data filtering pipeline with Ollama integration

- Add MergedDataJudge class for intelligent data filtering using Ollama Gemma
- Integrate automatic Ollama CLI installation into project setup process
- Add DATA_FILTERING step to training pipeline with concurrent processing
- Include testing for MergedDataJudge in its local main() function
- Add Ollama dependency to pyproject.toml

* feat: add automatic Ollama model cleanup after data filtering

* Add logging for outputting data filtering parameters

* fix: adjust error handling for MergedDataJudge:
- Keep original merged.json unchanged when any error occurs
- Exit filtering process immediately on errors instead of continuing with defaults
- Ensure training pipeline continues safely even if data filtering fails

* Add frontend for data filtering pipeline

* resolve data filtering quality_level error by commenting out problematic fields, change TrainProcessService back to original class definition

* fix: quote unquoted shade icons to prevent JSON parsing errors

* Fixed wiki_res.json missing due to no database connection at wiki/base.py module import

* Added scoring reasoning as part of the merged data

* fix: filter ANSI escape sequences from Ollama logs in data filtering step

* fix: Add data filtering steps to cloud training to resolve KeyError

- Added 'Data Filtering' step to cloud training progress holder
- Added data filtering step execution in cloud training service
- Added data filtering parameters to cloud training routes
- Updated frontend to send data filtering parameters
- Fixed missing except clause in cloud training service

This resolves the KeyError: 'data_filtering' when switching from cloud to local training.
2025-08-15 11:19:12 +08:00
..
api Data Filtering with Gemma (#396) 2025-08-15 11:19:12 +08:00
base remove additional data (#398) 2025-07-28 16:11:25 +08:00
common Data Filtering with Gemma (#396) 2025-08-15 11:19:12 +08:00
configs Fix: Ensure env_file is a Path (#94) 2025-03-31 15:05:41 +08:00
database Feature/cloud service (#383) 2025-06-04 19:52:14 +08:00
file_data Feature/new data (#393) 2025-07-01 15:39:04 +08:00
kernel Feature/new data (#393) 2025-07-01 15:39:04 +08:00
L0 feat(logging): separate training log (#83) 2025-03-27 10:12:11 +08:00
L1 Feature/new data (#393) 2025-07-01 15:39:04 +08:00
L2 Data Filtering with Gemma (#396) 2025-08-15 11:19:12 +08:00
models Feature/new data (#393) 2025-07-01 15:39:04 +08:00
stage1 Feature/new data (#393) 2025-07-01 15:39:04 +08:00
stage2 Data Filtering with Gemma (#396) 2025-08-15 11:19:12 +08:00
stage3 Data Filtering with Gemma (#396) 2025-08-15 11:19:12 +08:00
__init__.py Initial commit 2025-03-20 00:37:54 +08:00
app.py Added CUDA support (#228) 2025-04-25 10:20:36 +08:00
package-lock.json Initial commit 2025-03-20 00:37:54 +08:00
tokenizer.json Initial commit 2025-03-20 00:37:54 +08:00
utils.py Fix tiktoken compatibility with non-OpenAI models (#155) 2025-04-07 10:11:56 +08:00