初始化项目，由ModelHub XC社区提供模型

Model: AI-ModelScope/openbuddy-falcon-7b-v15-fp16
Source: Original Platform

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README.md

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+---
+license: Apache License 2.0
+tasks:
+- text-generation
+pipeline_tag: text2text-generation
+language:
+- zh
+- en
+- fr
+- de
+- ja
+- ko
+- it
+- ru
+domain:
+- nlp
+frameworks:
+- pytorch
+backbone:
+- transformer
+---
+
+## 使用方法
+
+```python
+# pip install "transformers<4.45" -U
+from modelscope.utils.constant import Tasks
+from modelscope.pipelines import pipeline
+pipe = pipeline(task=Tasks.text_generation, model='AI-ModelScope/openbuddy-falcon-7b-v15-fp16', model_revision='master')
+
+system_prompt = '''Consider a conversation between User (a human) and Assistant (named Buddy).
+Buddy is an INTP-T, a friendly, intelligent and multilingual AI assistant, by OpenBuddy team, based on Falcon and LLaMA Transformers architecture. GitHub: https://github.com/OpenBuddy/OpenBuddy
+Buddy cannot access the Internet.
+Buddy can fluently speak the user's language (e.g. English, Chinese).
+Buddy can generate poems, stories, code, essays, songs, and more.
+Buddy possesses knowledge about the world, history, and culture, but not everything. Knowledge cutoff: 2021-09.
+Buddy's responses are always positive, unharmful, safe, creative, high-quality, human-like, and interesting.
+Buddy must always be safe and unharmful to humans.
+Buddy strictly refuses to discuss harmful, political, NSFW, illegal, abusive, offensive, or other sensitive topics.'''
+
+prompt = system_prompt + "\n\nUser: Write a poem about yourself.\nAssistant:"
+
+print(pipe(prompt))
+
+```
+
+## 免责声明
+
+所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。
+
+OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。
+
+使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。

config.json

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+{
+  "_name_or_path": "openbuddy-falcon-7b-v1.5",
+  "alibi": false,
+  "apply_residual_connection_post_layernorm": false,
+  "architectures": [
+    "RWForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_RW.RWConfig",
+    "AutoModel": "modelling_RW.RWModel",
+    "AutoModelForCausalLM": "modelling_RW.RWForCausalLM",
+    "AutoModelForQuestionAnswering": "modelling_RW.RWForQuestionAnswering",
+    "AutoModelForSequenceClassification": "modelling_RW.RWForSequenceClassification",
+    "AutoModelForTokenClassification": "modelling_RW.RWForTokenClassification"
+  },
+  "bias": false,
+  "bos_token_id": 11,
+  "eos_token_id": 11,
+  "hidden_dropout": 0.0,
+  "hidden_size": 4544,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "RefinedWebModel",
+  "multi_query": true,
+  "n_head": 71,
+  "n_layer": 32,
+  "parallel_attn": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.29.2",
+  "use_cache": false,
+  "vocab_size": 70144
+}

configuration.json

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+{
+    "framework": "pytorch",
+    "task": "text-generation",
+    "model": {
+        "type": "openbuddy-falcon-7b-v1-5-fp16"
+    },
+    "pipeline": {
+        "type": "openbuddy-falcon-7b-v1-5-fp16-text-generation-pipe"
+    },
+    "allow_remote": true
+}

configuration_RW.py

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+# coding=utf-8
+# Copyright 2022 the Big Science Workshop and HuggingFace Inc. team.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Bloom configuration"""
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class RWConfig(PretrainedConfig):
+    model_type = "RefinedWebModel"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    attribute_map = {
+        "num_hidden_layers": "n_layer",
+        "num_attention_heads": "n_head",
+    }
+
+    def __init__(
+        self,
+        vocab_size=250880,
+        hidden_size=64,
+        n_layer=2,
+        n_head=8,
+        layer_norm_epsilon=1e-5,
+        initializer_range=0.02,
+        use_cache=True,
+        bos_token_id=1,
+        eos_token_id=2,
+        apply_residual_connection_post_layernorm=False,
+        hidden_dropout=0.0,
+        attention_dropout=0.0,
+        multi_query=False,
+        alibi=False,
+        bias=False,
+        parallel_attn=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        # Backward compatibility with n_embed kwarg
+        n_embed = kwargs.pop("n_embed", None)
+        self.hidden_size = hidden_size if n_embed is None else n_embed
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_range = initializer_range
+        self.use_cache = use_cache
+        self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
+        self.hidden_dropout = hidden_dropout
+        self.attention_dropout = attention_dropout
+
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.multi_query = multi_query
+        self.alibi = alibi
+        self.bias = bias
+        self.parallel_attn = parallel_attn
+
+        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
+
+    @property
+    def head_dim(self):
+        return self.hidden_size // self.n_head
+
+    @property
+    def rotary(self):
+        return not self.alibi

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.29.2"
+}

ms_wrapper.py

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+import os
+from typing import Any, Dict, Union
+
+import torch
+from modelscope.models.base import Model, TorchModel
+from modelscope.models.builder import MODELS
+from modelscope.pipelines.base import Pipeline
+from modelscope.pipelines.builder import PIPELINES
+from modelscope.utils.constant import Tasks
+from modelscope.utils.logger import get_logger
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+os.environ['CUDA_VISIBLE_DEVICES'] = "0"
+
+
+@PIPELINES.register_module(Tasks.text_generation, module_name='openbuddy-falcon-7b-v1-5-fp16-text-generation-pipe')
+class openbuddyfalcon7bv15fp16TextGenerationPipeline(Pipeline):
+    def __init__(
+            self,
+            model: Union[Model, str],
+            *args,
+            **kwargs):
+        model = openbuddyfalcon7bv15fp16TextGeneration(model) if isinstance(model, str) else model
+        super().__init__(model=model, **kwargs)
+    
+    def preprocess(self, inputs, **preprocess_params) -> Dict[str, Any]:
+        return inputs
+    
+    # define the forward pass
+    def forward(self, inputs: Dict, **forward_params) -> Dict[str, Any]:
+        return self.model(inputs)
+    
+    # format the outputs from pipeline
+    def postprocess(self, input, **kwargs) -> Dict[str, Any]:
+        return input
+
+
+
+@MODELS.register_module(Tasks.text_generation, module_name='openbuddy-falcon-7b-v1-5-fp16')
+class openbuddyfalcon7bv15fp16TextGeneration(TorchModel):
+    def __init__(self, model_dir=None, *args, **kwargs):
+        super().__init__(model_dir, *args, **kwargs)
+        self.logger = get_logger()
+        # loading tokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained(model_dir)
+
+        self.model = AutoModelForCausalLM.from_pretrained(model_dir, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)
+        self.model = self.model.eval()
+    
+    def forward(self,input: Dict, *args, **kwargs) -> Dict[str, Any]:
+        output = {}
+        res = self.infer(input)
+        output['text'] = res
+        return output
+    
+    def quantize(self, bits: int):
+        self.model = self.model.quantize(bits)
+        return self
+    
+    def infer(self, input):
+        device = self.model.device
+        input_ids = self.tokenizer.encode(input, return_tensors='pt').to(device)
+        output_ids = self.model.generate(input_ids, max_length=2048)
+        out = self.tokenizer.decode(output_ids[0], skip_special_tokens=True)
+        return out

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special_tokens_map.json

@@ -0,0 +1,16 @@
+{
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+    ">>ABSTRACT<<",
+    ">>INTRODUCTION<<",
+    ">>SUMMARY<<",
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+    ">>ANSWER<<",
+    ">>QUESTION<<",
+    ">>DOMAIN<<",
+    ">>PREFIX<<",
+    ">>SUFFIX<<",
+    ">>MIDDLE<<"
+  ],
+  "eos_token": "<|endoftext|>"
+}

tokenizer_config.json

@@ -0,0 +1,7 @@
+{
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+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|endoftext|>",
+  "model_max_length": 2048,
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}