YAML Metadata Warning: The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

This is LoRA checkpoint fine-tuned with the following CLI. The fine-tuning process is logged in W&B dashboard. I have used DGX workstation with 8 x A100(40G).

python finetune.py \
    --base_model='elinas/llama-65b-hf-transformers-4.29' \
    --data_path='alpaca_data.json' \
    --num_epochs=10 \
    --cutoff_len=1024 \
    --group_by_length \
    --output_dir='./lora-alpaca-65b-elinas' \
    --lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
    --lora_r=16 \
    --lora_alpha=32 \
    --batch_size=1024 \
    --micro_batch_size=15

This LoRA checkpoint is recommended to be used with transformers >= 4.29 which should be installed with the following command currently(4/30/2023).

pip install git+https://github.com/huggingface/transformers.git

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