Update NeedleInAHaystack Docs (open-compass#1102)

* update NeedleInAHaystack Test Docs

* update docs

configs/eval_needlebench.py

@@ -1,78 +1,26 @@
-from opencompass.models import HuggingFaceCausalLM
-from opencompass.models.turbomind import TurboMindModel
-from opencompass.runners import SlurmSequentialRunner
-from opencompass.partitioners import SizePartitioner, NaivePartitioner
-from opencompass.tasks import OpenICLInferTask, OpenICLEvalTask
-
 from mmengine.config import read_base
 with read_base():
-    # eval needlebench_4k
-    from .datasets.needlebench.needlebench_4k.needlebench import needlebench_datasets
-    from .summarizers.needlebench import needlebench_4k_summarizer as summarizer
+    from .models.hf_internlm.lmdeploy_internlm2_chat_7b import models as internlm2_chat_7b_200k
+    from .models.hf_internlm.hf_internlm2_chat_7b import models as internlm2_chat_7b
 
-    # only eval original "needle in a haystack test" in needlebench_4k
-    # from .datasets.needlebench.needlebench_4k.needlebench_single import needlebench_datasets_zh, needlebench_datasets_en
+    # Evaluate needlebench_4k, adjust the configuration to use 8k, 32k, 128k, 200k, or 1000k if necessary.
+    # from .datasets.needlebench.needlebench_4k.needlebench_4k import needlebench_datasets
     # from .summarizers.needlebench import needlebench_4k_summarizer as summarizer
 
+    # only eval original "needle in a haystack test" in needlebench_4k
+    from .datasets.needlebench.needlebench_4k.needlebench_single_4k import needlebench_zh_datasets, needlebench_en_datasets
+    from .summarizers.needlebench import needlebench_4k_summarizer as summarizer
+
     # eval Ancestral Tracing Challenge(ATC)
-    # from .datasets.needlebench.atc.atc import needlebench_atc_datasets_zh, needlebench_atc_datasets_en
-    # from .summarizers.needlebench import needlebench_atc_summarizer as summarizer
+    # from .datasets.needlebench.atc.atc_choice_50 import needlebench_datasets
+    # from .summarizers.needlebench import atc_summarizer_50 as summarizer
 
 datasets = sum([v for k, v in locals().items() if ('datasets' in k)], [])
 
-hf_internlm2_chat_7b_model_meta_template = dict(
-    round=[
-        dict(role='HUMAN',
-             begin='<|im_start|>user\n', end='<|im_end|>\n'),
-        dict(role='BOT', begin='<|im_start|>assistant\n',
-             end='<|im_end|>\n', generate=True),
-    ],
-)
-hf_internlm2_chat_7b = dict(
-        type=HuggingFaceCausalLM,
-        abbr='internlm2-chat-7b-hf',
-        path="internlm/internlm2-chat-7b",
-        tokenizer_path='internlm/internlm2-chat-7b',
-        model_kwargs=dict(
-            trust_remote_code=True,
-            device_map='auto',
-        ),
-        tokenizer_kwargs=dict(
-            padding_side='left',
-            truncation_side='left',
-            use_fast=False,
-            trust_remote_code=True,
-        ),
-        max_out_len=2000,
-        max_seq_len=32768,
-        batch_size=8,
-        meta_template=hf_internlm2_chat_7b_model_meta_template,
-        run_cfg=dict(num_gpus=1, num_procs=1),
-        end_str='<|im_end|>',
-        )
-
-internlm2_chat_7b_200k = dict(
-        type=TurboMindModel,
-        abbr='internlm2-chat-7b-200k',
-        path="internlm/internlm2-chat-7b",
-        meta_template=hf_internlm2_chat_7b_model_meta_template,
-        engine_config=dict(session_len=210000,
-                           max_batch_size=8,
-                           rope_scaling_factor=2.0,
-                           model_name="internlm2-chat-7b"),
-        gen_config=dict(top_k=1, top_p=0.8,
-                        temperature=1.0,
-                        max_new_tokens=2000),
-        max_out_len=2000,
-        max_seq_len=210000,
-        batch_size=8,
-        concurrency=8,
-        run_cfg=dict(num_gpus=1, num_procs=1),
-    )
+for m in internlm2_chat_7b:
+    m['max_seq_len'] = 32768 # Ensure InternLM2-7B model can receive the full length of long texts, adjust for other models based on their supported maximum sequence length.
+    m['max_out_len'] = 2000 # Ensure complete responses from the model in multi-needle retrieval tasks.
 
-models = [
-    # hf_internlm2_chat_7b,
-    internlm2_chat_7b_200k,
-]
+models = internlm2_chat_7b
 
 work_dir = './outputs/needlebench'