Comments (5)
@anthonyprinaldi It'd be great if we can start with the fine-tuning piece. The current requirements.txt file contains all the packages one needs to finetune the models so would be nice if we can dockerize that bit first.
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@sammcj We finally have a dockerfile added to the repository.
@benjaminye can help answer any questions you may have.
Please let us know if you have more feedback to give. And we are always looking for external collaborators :)
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Thanks for the feedback! We will work on including this file soon.
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@sammcj there are a few different tasks for the project (i.e., fine-tuning, evaluation, and inference). Which of these are of interest to you?
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@anthonyprinaldi personally I agree with Rohit, fine-tuning is something that currently isn't as easy for most folks to get into where as there's a lot of front ends for inference.
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Related Issues (20)
- CLI to Generate Example `config.yml`
- Add flash_attention_1/flash_attention_2 support & examples HOT 1
- `jsonl` support
- How do you handle history? HOT 2
- Add comment to indicate tf32 won't be available for older GPUs HOT 1
- `pipx` installation doesn't work HOT 1
- Allow custom train/test datasets
- Add ROADMAP section to the Readme HOT 1
- Change `infer_all` to `infer_test_set` for inference module
- Add unit tests for the code. HOT 1
- Remove unused `accelerate` code
- Allow users to set verbosity of outputs
- Make column variable format in prompt `{{ var }}` instead of `{ var }`
- Quickstart Basic uses a very large model and is slow. HOT 1
- Trying to access gated repo error, Quickstart Basic HOT 2
- quickstart basic - missing qa/llm_tests:? HOT 2
- example config file to run inference only on fine-tuned model HOT 2
- [CLI] Add `llmtune inference [experiment_dir]`
- JSON output test HOT 1
- cuda device-side runtime error when training on custom dataset for JSON outputs HOT 4
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