YAML Metadata Warning: The pipeline tag "conversational" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, 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-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, other

Model

internlm-20b-qlora-msagent-react is fine-tuned from InternLM-20B with MSAgent-Bench dataset by XTuner.

Quickstart

Usage with XTuner CLI

Installation

pip install xtuner

Chat

xtuner chat internlm/internlm-20b --adapter xtuner/internlm-20b-qlora-msagent-react --lagent

Fine-tune

Use the following command to quickly reproduce the fine-tuning results.

NPROC_PER_NODE=8 xtuner train internlm_20b_qlora_msagent_react_e3_gpu8

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