upload adapter

README.md

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+---
+library_name: peft
+pipeline_tag: conversational
+base_model: internlm/internlm-20b
+---
+
+<div align="center">
+  <img src="https://github.com/InternLM/lmdeploy/assets/36994684/0cf8d00f-e86b-40ba-9b54-dc8f1bc6c8d8" width="600"/>
+
+
+[![Generic badge](https://img.shields.io/badge/GitHub-%20XTuner-black.svg)](https://github.com/InternLM/xtuner)
+
+
+</div>
+
+## Model
+
+internlm-20b-qlora-msagent-react is fine-tuned from [InternLM-20B](https://huggingface.co/internlm/internlm-20b) with [MSAgent-Bench](https://modelscope.cn/datasets/damo/MSAgent-Bench) dataset by [XTuner](https://github.com/InternLM/xtuner).
+
+
+## Quickstart
+
+### Usage with XTuner CLI
+
+#### Installation
+
+```shell
+pip install xtuner
+```
+
+#### Chat
+
+```shell
+xtuner chat internlm/internlm-20b --adapter xtuner/internlm-20b-qlora-msagent-react --lagent
+```
+
+#### Fine-tune
+
+Use the following command to quickly reproduce the fine-tuning results.
+
+```shell
+NPROC_PER_NODE=8 xtuner train internlm_20b_qlora_msagent_react_e3_gpu8
+```

adapter_config.json

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+{
+  "auto_mapping": null,
+  "base_model_name_or_path": "/140/InternLM/20B/internlm-20b-base",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "lora_alpha": 16,
+  "lora_dropout": 0.1,
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 64,
+  "revision": null,
+  "target_modules": [
+    "k_proj",
+    "down_proj",
+    "q_proj",
+    "up_proj",
+    "gate_proj",
+    "v_proj",
+    "o_proj"
+  ],
+  "task_type": "CAUSAL_LM"
+}

adapter_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ad0cff2f1efe74a1bf77e6667183b40cf547b1e4ed2490ef3529ca0091324989
+size 751345965

xtuner_config.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from bitsandbytes.optim import PagedAdamW32bit
+from mmengine.dataset import DefaultSampler
+from mmengine.hooks import (CheckpointHook, DistSamplerSeedHook, IterTimerHook,
+                            LoggerHook, ParamSchedulerHook)
+from mmengine.optim import AmpOptimWrapper, CosineAnnealingLR
+from modelscope.msdatasets import MsDataset
+from peft import LoraConfig
+from transformers import (AutoModelForCausalLM, AutoTokenizer,
+                          BitsAndBytesConfig)
+
+from xtuner.dataset import process_ms_dataset
+from xtuner.dataset.collate_fns import default_collate_fn
+from xtuner.dataset.map_fns import msagent_react_map_fn
+from xtuner.engine import DatasetInfoHook, EvaluateChatHook
+from xtuner.model import SupervisedFinetune
+
+#######################################################################
+#                          PART 1  Settings                           #
+#######################################################################
+# Model
+pretrained_model_name_or_path = '/140/InternLM/20B/internlm-20b-base'
+
+# Data
+data_path = 'damo/MSAgent-Bench'
+max_length = 2048
+pack_to_max_length = False
+
+# Scheduler & Optimizer
+batch_size = 8  # per_device
+accumulative_counts = 1
+dataloader_num_workers = 2
+max_epochs = 3
+optim_type = PagedAdamW32bit
+lr = 2e-4
+betas = (0.9, 0.999)
+weight_decay = 0
+max_norm = 1  # grad clip
+
+# Evaluate the generation performance during the training
+evaluation_freq = 500
+evaluation_inputs = [
+    ('<|System|>:你是一个可以调用外部工具的助手，可以使用的工具包括：\n'
+     "{\'GoogleSearch\': \'一个可以从谷歌搜索结果的API。\\n"
+     '当你需要对于一个特定问题找到简短明了的回答时，可以使用它。\\n'
+     "输入应该是一个搜索查询。\\n\\n输入参数:{'query': '搜索查询'}\',"
+     "\'PythonInterpreter\': \"用来执行Python代码。代码必须是一个函数，\\n"
+     "函数名必须得是 \'solution\'，代码对应你的思考过程。代码实例格式如下：\\n"
+     '```python\\n# import 依赖包\\nimport xxx\\ndef solution():'
+     '\\n    # 初始化一些变量\\n    variable_names_with_real_meaning = xxx'
+     '\\n    # 步骤一\\n    mid_variable = func(variable_names_with_real_meaning)'
+     '\\n    # 步骤 x\\n    mid_variable = func(mid_variable)\\n    # 最后结果'
+     '\\n    final_answer =  func(mid_variable)\\n    return final_answer'
+     "\\n```\\n输入参数:{'command': '需要执行的代码'}\"}\n"
+     '如果使用工具请遵循以下格式回复：\n```\n'
+     'Thought:思考你当前步骤需要解决什么问题，是否需要使用工具\n'
+     "Action:工具名称，你的工具必须从 [[\'GoogleSearch\', \'PythonInterpreter\']] 选择"
+     '\nAction Input:工具输入参数\n```\n工具返回按照以下格式回复：\n'
+     '```\nResponse:调用工具后的结果\n```'
+     '\n如果你已经知道了答案，或者你不需要工具，请遵循以下格式回复\n```'
+     '\nThought:给出最终答案的思考过程\nFinal Answer:最终答案\n```\n开始!\n'
+     '<|User|>:上海明天天气怎么样？\n'
+     '<|Bot|>:')
+]
+
+#######################################################################
+#                      PART 2  Model & Tokenizer                      #
+#######################################################################
+tokenizer = dict(
+    type=AutoTokenizer.from_pretrained,
+    pretrained_model_name_or_path=pretrained_model_name_or_path,
+    trust_remote_code=True,
+    padding_side='right')
+
+model = dict(
+    type=SupervisedFinetune,
+    llm=dict(
+        type=AutoModelForCausalLM.from_pretrained,
+        pretrained_model_name_or_path=pretrained_model_name_or_path,
+        trust_remote_code=True,
+        torch_dtype=torch.float16,
+        quantization_config=dict(
+            type=BitsAndBytesConfig,
+            load_in_4bit=True,
+            load_in_8bit=False,
+            llm_int8_threshold=6.0,
+            llm_int8_has_fp16_weight=False,
+            bnb_4bit_compute_dtype=torch.float16,
+            bnb_4bit_use_double_quant=True,
+            bnb_4bit_quant_type='nf4')),
+    lora=dict(
+        type=LoraConfig,
+        r=64,
+        lora_alpha=16,
+        lora_dropout=0.1,
+        bias='none',
+        task_type='CAUSAL_LM'))
+
+#######################################################################
+#                      PART 3  Dataset & Dataloader                   #
+#######################################################################
+train_dataset = dict(
+    type=process_ms_dataset,
+    dataset=dict(type=MsDataset.load, dataset_name=data_path),
+    tokenizer=tokenizer,
+    max_length=max_length,
+    dataset_map_fn=msagent_react_map_fn,
+    remove_unused_columns=True,
+    shuffle_before_pack=True,
+    pack_to_max_length=pack_to_max_length)
+
+train_dataloader = dict(
+    batch_size=batch_size,
+    num_workers=dataloader_num_workers,
+    dataset=train_dataset,
+    sampler=dict(type=DefaultSampler, shuffle=True),
+    collate_fn=dict(type=default_collate_fn))
+
+#######################################################################
+#                    PART 4  Scheduler & Optimizer                    #
+#######################################################################
+# optimizer
+optim_wrapper = dict(
+    type=AmpOptimWrapper,
+    optimizer=dict(
+        type=optim_type, lr=lr, betas=betas, weight_decay=weight_decay),
+    clip_grad=dict(max_norm=max_norm, error_if_nonfinite=False),
+    accumulative_counts=accumulative_counts,
+    loss_scale='dynamic',
+    dtype='float16')
+
+# learning policy
+# More information: https://github.com/open-mmlab/mmengine/blob/main/docs/en/tutorials/param_scheduler.md  # noqa: E501
+param_scheduler = dict(
+    type=CosineAnnealingLR,
+    eta_min=lr * 0.1,
+    by_epoch=True,
+    T_max=max_epochs,
+    convert_to_iter_based=True)
+
+# train, val, test setting
+train_cfg = dict(by_epoch=True, max_epochs=max_epochs, val_interval=1)
+
+#######################################################################
+#                           PART 5  Runtime                           #
+#######################################################################
+# Log the dialogue periodically during the training process, optional
+custom_hooks = [
+    dict(type=DatasetInfoHook, tokenizer=tokenizer),
+    dict(
+        type=EvaluateChatHook,
+        tokenizer=tokenizer,
+        every_n_iters=evaluation_freq,
+        evaluation_inputs=evaluation_inputs)
+]
+
+# configure default hooks
+default_hooks = dict(
+    # record the time of every iteration.
+    timer=dict(type=IterTimerHook),
+    # print log every 100 iterations.
+    logger=dict(type=LoggerHook, interval=10),
+    # enable the parameter scheduler.
+    param_scheduler=dict(type=ParamSchedulerHook),
+    # save checkpoint per epoch.
+    checkpoint=dict(type=CheckpointHook, interval=1),
+    # set sampler seed in distributed evrionment.
+    sampler_seed=dict(type=DistSamplerSeedHook),
+)
+
+# configure environment
+env_cfg = dict(
+    # whether to enable cudnn benchmark
+    cudnn_benchmark=False,
+    # set multi process parameters
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+    # set distributed parameters
+    dist_cfg=dict(backend='nccl'),
+)
+
+# set visualizer
+visualizer = None
+
+# set log level
+log_level = 'INFO'
+
+# load from which checkpoint
+load_from = None
+
+# whether to resume training from the loaded checkpoint
+resume = False
+
+# Defaults to use random seed and disable `deterministic`
+randomness = dict(seed=None, deterministic=False)