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7505b3cadf
* add TheoremQA with 5-shot * add huggingface_above_v4_33 classes * use num_worker partitioner in cli * update theoremqa * update TheoremQA * add TheoremQA * rename theoremqa -> TheoremQA * update TheoremQA output path * rewrite many model configs * update huggingface * further update * refine configs * update configs * update configs * add configs/eval_llama3_instruct.py * add summarizer multi faceted * update bbh datasets * update configs/models/hf_llama/lmdeploy_llama3_8b_instruct.py * rename class * update readme * update hf above v4.33
25 lines
813 B
Python
25 lines
813 B
Python
from opencompass.models import TurboMindModel
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_meta_template = dict(
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round=[
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dict(role="HUMAN", begin='<|begin_of_text|>user<|end_header_id|>\n\n', end='<|eot_id|>'),
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dict(role="BOT", begin='<|begin_of_text|>assistant<|end_header_id|>\n\n', end='<|eot_id|>', generate=True),
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],
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)
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models = [
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dict(
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type=TurboMindModel,
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abbr='llama-3-8b-instruct-lmdeploy',
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path='meta-llama/Meta-Llama-3-8B-Instruct',
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engine_config=dict(session_len=4096, max_batch_size=16, tp=1),
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gen_config=dict(top_k=1, temperature=1, top_p=0.9, max_new_tokens=1024, stop_words=[128001, 128009]),
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max_out_len=1024,
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max_seq_len=4096,
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batch_size=16,
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concurrency=16,
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meta_template=_meta_template,
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run_cfg=dict(num_gpus=1),
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)
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]
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