Give h2oGPT proper personality

[arnocandel]

Fix https://github.com/h2oai/h2ogpt/blob/main/FAQ.md#why-does-the-h2ogpt-say-it-was-trained-by-openai-or-open-assistant

[arnocandel]

h2ogpt/create_data.py

Line 1252 in 97b9f2d

def test_add_open_assistant(fixup_personality, only_personality, save_json=True):

(env) arno@rippa:/nfs4/llm/h2ogpt(main)$ NCCL_P2P_LEVEL=LOC WORLD_SIZE=2 CUDA_VISIBLE_DEVICES="0,1" torchrun --nproc_per_node=2 finetune.py --base_model=h2oai/h2ogpt-oig-oasst1-512-6.9b --data_path=h2oai/openassistant_oasst1_h2ogpt_only --micro_batch_size=2 --batch_size=32 --cutoff_len=2048 --run_id=100 --num_epochs=1 &> run.100.text

https://slack-files.com/T0329MHH6-F054U3491DF-a909f3bb3b lora weights and logs

[arnocandel]

Comparing validation score on only 1k ShareGPT before/after personality refresh, so just "hammering" the personality updates, and nothing else.

before:
CUDA_VISIBLE_DEVICES=1 python generate.py --base_model=h2oai/h2ogpt-oig-oasst1-512-6.9b --prompt_type='human_bot' --chat=False --stream_output=False --gradio=False --eval_sharegpt_prompts_only=1000 --eval_sharegpt_as_output=False --num_beams=1 &> h2ogpt-oig-oasst1-512-6.9b.eval.log
h2ogpt-oig-oasst1-512-6.9b.eval.log

after:
CUDA_VISIBLE_DEVICES=0 python generate.py --base_model=h2oai/h2ogpt-oig-oasst1-512-6.9b --lora_weights=h2ogpt-oig-oasst1-512-6.9b.h2oaiopenassistant_oasst1_h2ogpt_only.1_epochs.97b9f2df6851e8b1c2f220a1028adad9029140bf.100 --prompt_type='human_bot' --chat=False --stream_output=False --gradio=False --eval_sharegpt_prompts_only=1000 --eval_sharegpt_as_output=False --num_beams=1 &> h2ogpt-oig-oasst1-512-6.9b.h2oaiopenassistant_oasst1_h2ogpt_only.1_epochs.97b9f2df6851e8b1c2f220a1028adad9029140bf.100.eval.log
h2ogpt-oig-oasst1-512-6.9b.h2oaiopenassistant_oasst1_h2ogpt.2_epochs.e35e2e06e0af2f7dceac2e16e3646c90ccce4ec0.1.eval.log

[arnocandel]

give h2oai/h2ogpt-oasst1-512-6.9b personality

So obviously hurts too much to only hammer in the personality and nothing else, need to keep variety of fine-tuning data, so easiest for smaller models is to train a couple epochs on the full personalized dataset instead
https://huggingface.co/datasets/h2oai/openassistant_oasst1_h2ogpt

For larger models, can try to do some mixin mode, with small personality data + large mixin, but not all 80k rows. Note: didn't do that, just added 2 epochs on top of existing *512* models.

torchrun --nproc_per_node=2 finetune.py --base_model=h2oai/h2ogpt-oig-oasst1-512-6.9b --data_path=h2oai/openassistant_oasst1_h2ogpt --prompt_type=plain --run_id=1 --micro_batch_size=8 --batch_size=512 --cutoff_len=512 --num_epochs=2 &> log.1.txt

https://slack-files.com/T0329MHH6-F054GHUJJFM-eebaba5e98 lora weights and logs

https://huggingface.co/h2oai/h2ogpt-oig-oasst1-512-6.9b model card created by fcaae7e

[arnocandel]

CUDA_VISIBLE_DEVICES=0 python generate.py --base_model=h2oai/h2ogpt-oig-oasst1-512-6.9b --lora_weights=h2ogpt-oig-oasst1-512-6.9b.h2oaiopenassistant_oasst1_h2ogpt.2_epochs.e35e2e06e0af2f7dceac2e16e3646c90ccce4ec0.1 --prompt_type='human_bot' --chat=False --stream_output=False --gradio=False --eval_sharegpt_prompts_only=1000 --eval_sharegpt_as_output=False --num_beams=1 &> h2ogpt-oig-oasst1-512-6.9b.h2oaiopenassistant_oasst1_h2ogpt.2_epochs.e35e2e06e0af2f7dceac2e16e3646c90ccce4ec0.1.eval.log

[arnocandel]

give h2oai/h2ogpt-oasst1-512-12b personality

continue #22 (comment)

torchrun --nproc_per_node=2 finetune.py --base_model=h2oai/h2ogpt-oasst1-512-12b --data_path=h2oai/openassistant_oasst1_h2ogpt --prompt_type=plain --run_id=2 --micro_batch_size=4 --batch_size=128 --cutoff_len=512 --num_epochs=2 &> log.2.txt

[arnocandel]

Comparing validation score on 1k ShareGPT before/after personality refresh

Before:
CUDA_VISIBLE_DEVICES=0,1 python generate.py --base_model=h2oai/h2ogpt-oasst1-512-12b --prompt_type='human_bot' --infer_devices=False --chat=False --stream_output=False --gradio=False --eval_sharegpt_prompts_only=1000 --eval_sharegpt_as_output=False --num_beams=1 &> h2ogpt-oasst1-512-12b.eval.log
h2ogpt-oasst1-512-12b.eval.log

After:
CUDA_VISIBLE_DEVICES=0,1 python generate.py --base_model=h2oai/h2ogpt-oasst1-512-12b --lora_weights=h2ogpt-oasst1-512-12b.h2oaiopenassistant_oasst1_h2ogpt.2_epochs.fcaae7ef70600de8c97c9b38cb3f0075467cdad1.2 --prompt_type='human_bot' --infer_devices=False --chat=False --stream_output=False --gradio=False --eval_sharegpt_prompts_only=1000 --eval_sharegpt_as_output=False --num_beams=1 &> h2ogpt-oasst1-512-12b.h2oaiopenassistant_oasst1_h2ogpt.2_epochs.fcaae7ef70600de8c97c9b38cb3f0075467cdad1.2.eval.log
h2ogpt-oasst1-512-12b.h2oaiopenassistant_oasst1_h2ogpt.2_epochs.fcaae7ef70600de8c97c9b38cb3f0075467cdad1.2.eval.log

[arnocandel]

give h2oai/h2ogpt-oasst1-512-20b personality

torchrun --nproc_per_node=8 finetune.py --base_model=h2oai/h2ogpt-oasst1-512-20b --data_path=h2oai/openassistant_oasst1_h2ogpt --prompt_type=plain --run_id=3 --micro_batch_size=4 --batch_size=128 --cutoff_len=512 --num_epochs=2 &> log.3.txt

https://slack-files.com/T0329MHH6-F054HBLG2LB-7a39e222f8 lora weights and logs

h2oai/h2ogpt-oasst1-512-20b e0202c5

[arnocandel]

dcb56af