torchrun --nproc-per-node=4 --no-python lm_eval --model nemo_lm --model_args path=<path_to_nemo_model>,devices=4,tensor_model_parallel_size=2,pipeline_model_parallel_size=2 --tasks hellaswag --batch_size 32 请注意,建议将python命令替换为torchrun --nproc-per-node=<设备数> --no-py...
func2_args = response.choices[0].message.tool_calls[0].function.arguments func2_out =eval(f'{func2_name}(**{func2_args})')print(func2_out) 运行python代码:python /root/internlm2_5_func.py。 终端输出如下。 我们可以看出InternLM2.5将输入'Compute (3+5)*2'根据提供的function拆分成了"加...
model = AutoModelForCausalLM.from_pretrained("/root/internlm2-chat-1_8b", torch_dtype=torch.float16, trust_remote_code=True).cuda() model = model.eval() inp = "hello" print("[INPUT]", inp) response, history = model.chat(tokenizer, inp, history=[]) print("[OUTPUT]", response) ...
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map='cuda:0') model = model.eval() system_prompt = """You are an AI assistant whose name is InternLM (书生·浦语). - InternLM (书生·浦语) is a conversatio...
首先进入一个你想要存放模型的目录,本教程统一放置在Home目录。执行如下指令: 代码语言:javascript 复制 cd~ 然后执行如下指令由开发机的共享目录软链接或拷贝模型: 代码语言:javascript 复制 ln-s/root/share/new_models/Shanghai_AI_Laboratory/internlm2-chat-1_8b/root/# cp-r/root/share/new_models/Shanghai...
eval() # warmup inp = "hello" for i in range(5): print("Warm up...[{}/5]".format(i+1)) response, history = model.chat(tokenizer, inp, history=[]) # test speed inp = "请介绍一下你自己。" times = 10 total_words = 0 start_time = datetime.datetime.now() for i in ...
有效支持20万字超长上下文：模型在20万字长输入中几乎完美地实现长文“大海捞针”，而且在 LongBench 和 L-Eval 等长文任务中的表现也达到开源模型中的领先水平。 可以通过 LMDeploy 尝试20万字超长上下文推理。综合性能全面提升：各能力维度相比上一代模型全面进步，在推理、数学、代码、对话体验、指令遵循和创意写作等...
为方便文件管理,我们需要一个存放模型的目录,本教程统一放置在/root/models/目录。 运行以下命令,创建文件夹并设置开发机共享目录的软链接。 代码语言:javascript 复制 mkdir/root/models ln-s/root/share/new_models//Shanghai_AI_Laboratory/internlm2_5-7b-chat /root/modelsln-s/root/share/new_models/OpenGV...
1024--micro-batch-size 12--global-batch-size 192--lr 0.0005--train-iters 150000--lr-decay-iters 150000--lr-decay-style cosine--lr-warmup-iters 2000--weight-decay .1--adam-beta2 .999--fp16--log-interval 10--save-interval 2000--eval-interval 200--eval-iters 10"TENSORBOARD_ARGS="...
eval() inp = "hello" print("[INPUT]", inp) response, history = model.chat(tokenizer, inp, history=[]) print("[OUTPUT]", response) inp = "please provide three suggestions about time management" print("[INPUT]", inp) response, history = model.chat(tokenizer, inp, history=history) ...