chore: import upstream snapshot with attribution

examples/eval/eval_url/demo.py

@@ -0,0 +1,15 @@
+# Copyright (c) ModelScope Contributors. All rights reserved.
+import os
+
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
+
+if __name__ == '__main__':
+    from swift import DeployArguments, EvalArguments, eval_main, run_deploy
+
+    # Here's a runnable demo provided. Use the eval_url method for evaluation.
+    # In a real scenario, you can simply remove the deployed context.
+    print(EvalArguments.list_eval_dataset())
+    with run_deploy(
+            DeployArguments(model='Qwen/Qwen2.5-0.5B-Instruct', verbose=False, log_interval=-1, infer_backend='vllm'),
+            return_url=True) as url:
+        eval_main(EvalArguments(model='Qwen2.5-0.5B-Instruct', eval_url=url, eval_dataset=['arc']))

examples/eval/eval_url/eval.sh

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+# You need to have a deployed model or api service first
+swift eval \
+    --model '<model_name>' \
+    --eval_backend OpenCompass \
+    --eval_url http://127.0.0.1:8000/v1 \
+    --eval_limit 100 \
+    --eval_dataset gsm8k

examples/eval/llm/sglang.sh

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+CUDA_VISIBLE_DEVICES=0 \
+swift eval \
+    --model Qwen/Qwen2.5-1.5B-Instruct \
+    --eval_backend OpenCompass \
+    --infer_backend sglang \
+    --eval_limit 100 \
+    --eval_dataset gsm8k

examples/eval/llm/vllm.sh

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+CUDA_VISIBLE_DEVICES=0 \
+swift eval \
+    --model Qwen/Qwen2.5-1.5B-Instruct \
+    --eval_backend OpenCompass \
+    --infer_backend vllm \
+    --eval_limit 100 \
+    --eval_dataset gsm8k

examples/eval/train_eval/train.sh

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+CUDA_VISIBLE_DEVICES=0 \
+swift sft \
+  --model "Qwen/Qwen2.5-0.5B-Instruct" \
+  --tuner_type "lora" \
+  --dataset "AI-ModelScope/alpaca-gpt4-data-zh#100" \
+  --torch_dtype "bfloat16" \
+  --num_train_epochs "1" \
+  --per_device_train_batch_size "1" \
+  --learning_rate "1e-4" \
+  --lora_rank "8" \
+  --lora_alpha "32" \
+  --target_modules "all-linear" \
+  --gradient_accumulation_steps "16" \
+  --save_steps "50" \
+  --save_total_limit "5" \
+  --logging_steps "5" \
+  --max_length "2048" \
+  --eval_strategy "steps" \
+  --eval_steps "5" \
+  --per_device_eval_batch_size "5" \
+  --eval_use_evalscope \
+  --eval_dataset "gsm8k" \
+  --eval_dataset_args '{"gsm8k": {"few_shot_num": 0}}' \
+  --eval_limit "10"

examples/eval/vlm/eval.sh

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+CUDA_VISIBLE_DEVICES=0 \
+MAX_PIXELS=1003520 \
+swift eval \
+  --model Qwen/Qwen2.5-VL-3B-Instruct \
+  --infer_backend vllm \
+  --eval_limit 100 \
+  --eval_dataset realWorldQA \
+  --eval_backend VLMEvalKit