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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, image-text-to-image, image-text-to-video, 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
GGUF quantized version of OLMo-7B-0724-Instruct Model
project original source: base model
Q_2_K (not nice)
Q_3_K_S (acceptable)
Q_3_K_M is acceptable (good for running with CPU)
Q_3_K_L (acceptable)
Q_4_K_S (okay)
Q_4_K_M is recommanded (balance)
Q_5_K_S (good)
Q_5_K_M (good in general)
Q_6_K is good also; if you want a better result; take this one instead of Q_5_K_M
Q_8_0 which is very good; need a reasonable size of RAM otherwise you might expect a long wait
f16 is similar to the original hf model; opt this one or hf also fine; make sure you have a good machine
how to run it
use any connector for interacting with gguf; i.e., gguf-connector
this picture is from the base model- Downloads last month
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Model tree for calcuis/olmo-gguf
Base model
allenai/OLMo-7B-0724-Instruct-hf