参数说明: - --model-args: 模型参数,以逗号分隔,key=value形式 - --datasets: 数据集名称,支持输入多个数据集,使用空格分开,参考下文数据集列表章节- --use-cache: 是否使用本地缓存,默认为false;如果为true,则已经评估过的模型和数据集组合将不会再次评估,直接从本地缓存读取 - --dataset-args: 数据集的...
Let's evaluate A: A = True and False = False. Let's evaluate B: B = not True and True = not (True and True) = not (True) = False. Plugging in A and B, we get: Z = A and B = False and False = False. So the answer is False. 模型预测 Generate 代码语言:javascript 代...
eval_dataset, eval_config, tokenizer, accuracy_metric, postprocess_mnli_predictions, ) 运行微调 现在,我们可以用下面一行运行微调: trainer.train() 运行验证 最后,我们在MNLI数据集的validation_mismatched拆分上验证我们的模型。微调500步后,模型的准确率应达到87%。 trainer.evaluate() 转换为Hugging Face检查点...
dataset= Dataset.from_dict(data) 最后一步直接调用现成接口评估: fromragasimportevaluatefromragas.metricsimport( faithfulness, answer_relevancy, context_relevancy, context_recall, context_precision, ) result=evaluate( dataset=dataset, metrics=[ context_precision, context_recall, faithfulness, answer_relevancy...
python -m awq.entry --model_path /dataset/llama-hf/$MODEL \ --w_bit 4 --q_group_size 128 \ --load_awq awq_cache/$MODEL-w4-g128.pt \ --q_backend real --dump_quant quant_cache/$MODEL-w4-g128-awq.pt #load and evaluate the real quantized model (smaller gpu memory usage) ...
fromdspy.evaluateimportEvaluate # Set up the LM turbo = dspy.OpenAI(model='gpt-3.5-turbo-instruct', max_tokens=250) dspy.settings.configure(lm=turbo) # 载入训练数据 gsm8k = GSM8K() gsm8k_trainset, gsm8k_devset = gsm8k.train[:10], gsm8k.dev[:1...
MODEL=llama-7b#run AWQ search (optional; we provided the pre-computed results)python -m awq.entry --model_path /dataset/llama-hf/$MODEL \ --w_bit4--q_group_size128\ --run_awq --dump_awq awq_cache/$MODEL-w4-g128.pt#evaluate the AWQ quantize model (simulated pseudo quantization...
https://cloud.google.com/vertex-ai/docs/generative-ai/models/evaluate-models?hl=zh-cn 7.Amazon Bedrock Amazon Bedrock支持用于大模型的评估。模型评估作业的执行结果可以用于对比选型,帮助选择最适合下游生成式AI模型。模型评估作业支持大型语言模型(LLM)的常见功能,例如:文本生成、文本分类、问答和文本摘要等。
python -m awq.entry --model_path /dataset/llama-hf/$MODEL \ --w_bit 4 --q_group_size 128 \ --run_awq --dump_awq awq_cache/$MODEL-w4-g128.pt #evaluate the AWQ quantize model (simulated pseudo quantization) python -m awq.entry --model_path /dataset/llama-hf/$MODEL \ ...
entry --model_path /dataset/llama-hf/$MODEL \ --w_bit 4 --q_group_size 128 \ --run_awq --dump_awq awq_cache/$MODEL-w4-g128.pt #evaluate the AWQ quantize model (simulated pseudo quantization) python -m awq.entry --model_path /dataset/llama-hf/$MODEL \ --tasks wikitext \...