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Model: FreedomIntelligence/Apollo2-3.8B
Source: Original Platform

.gitattributes

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CODE_OF_CONDUCT.md

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+# Microsoft Open Source Code of Conduct
+
+This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
+
+Resources:
+
+- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/)
+- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
+- Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns

LICENSE

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+Microsoft.
+Copyright (c) Microsoft Corporation.
+
+MIT License
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

NOTICE.md

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+NOTICES AND INFORMATION
+Do Not Translate or Localize
+
+This software incorporates material from third parties.
+
+**Component.** https://github.com/Dao-AILab/flash-attention
+
+**Open Source License/Copyright Notice.**
+
+BSD 3-Clause License
+
+Copyright (c) 2022, the respective contributors, as shown by the AUTHORS file.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

README.md

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+---
+license: apache-2.0
+datasets:
+- FreedomIntelligence/ApolloMoEDataset
+language:
+- ar
+- en
+- zh
+- ko
+- ja
+- mn
+- th
+- vi
+- lo
+- mg
+- de
+- pt
+- es
+- fr
+- ru
+- it
+- hr
+- gl
+- cs
+- co
+- la
+- uk
+- bs
+- bg
+- eo
+- sq
+- da
+- sa
+- gn
+- sr
+- sk
+- gd
+- lb
+- hi
+- ku
+- mt
+- he
+- ln
+- bm
+- sw
+- ig
+- rw
+- ha
+metrics:
+- accuracy
+base_model:
+- microsoft/Phi-3-mini-4k-instruct
+pipeline_tag: question-answering
+tags:
+- biology
+- medical
+---
+# Democratizing Medical LLMs For Much More Languages
+
+Covering 12 Major Languages including English, Chinese, French, Hindi, Spanish, Arabic, Russian, Japanese, Korean, German, Italian, Portuguese and 38 Minor Languages So far.
+
+
+
+<p align="center">
+   📃 <a href="https://arxiv.org/abs/2410.10626" target="_blank">Paper</a> • 🌐 <a href="" target="_blank">Demo</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEDataset" target="_blank">ApolloMoEDataset</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEBench" target="_blank">ApolloMoEBench</a>  • 🤗 <a href="https://huggingface.co/collections/FreedomIntelligence/apollomoe-and-apollo2-670ddebe3bb1ba1aebabbf2c" target="_blank">Models</a>  •🌐 <a href="https://github.com/FreedomIntelligence/Apollo" target="_blank">Apollo</a>  • 🌐 <a href="https://github.com/FreedomIntelligence/ApolloMoE" target="_blank">ApolloMoE</a>
+</p>
+
+
+
+![Apollo](assets/apollo_medium_final.png)
+
+
+## 🌈 Update
+
+* **[2024.10.15]** ApolloMoE repo is published！🎉
+
+
+## Languages Coverage
+12 Major Languages and 38 Minor Languages
+
+<details>
+  <summary>Click to view the Languages Coverage</summary>
+   
+   ![ApolloMoE](assets/languages.png)
+
+</details>
+
+
+## Architecture
+
+<details>
+  <summary>Click to view the MoE routing image</summary>
+
+  ![ApolloMoE](assets/hybrid_routing.png)
+
+</details>
+
+## Results
+
+#### Dense
+   🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-0.5B" target="_blank">Apollo2-0.5B</a>  • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-1.5B" target="_blank">Apollo2-1.5B</a>  • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-2B" target="_blank">Apollo2-2B</a>  
+   
+   🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-3.8B" target="_blank">Apollo2-3.8B</a>  • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-7B" target="_blank">Apollo2-7B</a>  • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo2-9B" target="_blank">Apollo2-9B</a>  
+
+<details>
+  <summary>Click to view the Dense Models Results</summary>
+
+   ![ApolloMoE](assets/dense_results.png)
+
+</details>
+
+
+#### Post-MoE
+   🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MoE-0.5B" target="_blank">Apollo-MoE-0.5B</a>  • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MoE-1.5B" target="_blank">Apollo-MoE-1.5B</a>  • 🤗 <a href="https://huggingface.co/FreedomIntelligence/Apollo-MoE-7B" target="_blank">Apollo-MoE-7B</a>  
+
+<details>
+  <summary>Click to view the Post-MoE Models Results</summary>
+
+   ![ApolloMoE](assets/post_moe_results.png)
+
+</details>
+
+   
+  
+
+## Usage Format
+##### Apollo2
+- 0.5B, 1.5B, 7B: User:{query}\nAssistant:{response}<|endoftext|>
+- 2B, 9B: User:{query}\nAssistant:{response}\<eos\>
+- 3.8B: <|user|>\n{query}<|end|><|assisitant|>\n{response}<|end|>
+
+##### Apollo-MoE
+- 0.5B, 1.5B, 7B: User:{query}\nAssistant:{response}<|endoftext|>
+  
+## Dataset & Evaluation
+
+- Dataset
+  🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEDataset" target="_blank">ApolloMoEDataset</a>
+
+   <details><summary>Click to expand</summary>
+
+    ![ApolloMoE](assets/Dataset.png)
+
+    - [Data category](https://huggingface.co/datasets/FreedomIntelligence/ApolloCorpus/tree/main/train)
+
+
+   </details>
+
+- Evaluation
+  🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/ApolloMoEBench" target="_blank">ApolloMoEBench</a> 
+
+   <details><summary>Click to expand</summary>
+  
+     - EN:
+       - [MedQA-USMLE](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options) 
+       - [MedMCQA](https://huggingface.co/datasets/medmcqa/viewer/default/test)
+       - [PubMedQA](https://huggingface.co/datasets/pubmed_qa): Because the results fluctuated too much, they were not used in the paper.
+       - [MMLU-Medical](https://huggingface.co/datasets/cais/mmlu)
+         - Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
+     - ZH:
+       - [MedQA-MCMLE](https://huggingface.co/datasets/bigbio/med_qa/viewer/med_qa_zh_4options_bigbio_qa/test)
+       - [CMB-single](https://huggingface.co/datasets/FreedomIntelligence/CMB): Not used in the paper
+         - Randomly sample 2,000 multiple-choice questions with single answer.
+       - [CMMLU-Medical](https://huggingface.co/datasets/haonan-li/cmmlu)
+         - Anatomy, Clinical_knowledge, College_medicine, Genetics, Nutrition, Traditional_chinese_medicine, Virology
+       - [CExam](https://github.com/williamliujl/CMExam): Not used in the paper
+         - Randomly sample 2,000 multiple-choice questions
+
+
+     - ES: [Head_qa](https://huggingface.co/datasets/head_qa)
+     - FR:
+       - [Frenchmedmcqa](https://github.com/qanastek/FrenchMedMCQA)
+       - [MMLU_FR]
+         - Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
+     - HI: [MMLU_HI](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Hindi)
+        - Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
+     - AR: [MMLU_AR](https://huggingface.co/datasets/FreedomIntelligence/MMLU_Arabic)
+        - Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
+     - JA: [IgakuQA](https://github.com/jungokasai/IgakuQA)
+     - KO: [KorMedMCQA](https://huggingface.co/datasets/sean0042/KorMedMCQA)
+     - IT:
+       - [MedExpQA](https://huggingface.co/datasets/HiTZ/MedExpQA)
+       - [MMLU_IT]
+         - Clinical knowledge, Medical genetics, Anatomy, Professional medicine, College biology, College medicine
+     - DE: [BioInstructQA](https://huggingface.co/datasets/BioMistral/BioInstructQA): German part
+     - PT: [BioInstructQA](https://huggingface.co/datasets/BioMistral/BioInstructQA): Portuguese part
+     - RU: [RuMedBench](https://github.com/sb-ai-lab/MedBench)
+
+  
+
+
+   </details>
+## Model Download and Inference
+   We take Apollo-MoE-0.5B as an example
+   1. Login Huggingface
+      
+       ```
+       huggingface-cli login --token $HUGGINGFACE_TOKEN
+       ```
+       
+   2. Download model to local dir
+        
+       ```python
+       from huggingface_hub import snapshot_download
+       import os
+
+       local_model_dir=os.path.join('/path/to/models/dir','Apollo-MoE-0.5B')
+       snapshot_download(repo_id="FreedomIntelligence/Apollo-MoE-0.5B", local_dir=local_model_dir)
+       ```
+       
+   3. Inference Example
+
+      ```python
+      from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
+      import os
+      
+      local_model_dir=os.path.join('/path/to/models/dir','Apollo-MoE-0.5B')
+      
+      model=AutoModelForCausalLM.from_pretrained(local_model_dir,trust_remote_code=True)
+      tokenizer = AutoTokenizer.from_pretrained(local_model_dir,trust_remote_code=True)
+      generation_config = GenerationConfig.from_pretrained(local_model_dir, pad_token_id=tokenizer.pad_token_id, num_return_sequences=1, max_new_tokens=7, min_new_tokens=2, do_sample=False, temperature=1.0, top_k=50, top_p=1.0)
+      
+      inputs = tokenizer('Answer direclty.\nThe capital of Mongolia is Ulaanbaatar.\nThe capital of Iceland is Reykjavik.\nThe capital of Australia is', return_tensors='pt')
+      inputs = inputs.to(model.device)
+      pred = model.generate(**inputs,generation_config=generation_config)
+      print(tokenizer.decode(pred.cpu()[0], skip_special_tokens=True))
+      ```
+
+## Results reproduction
+   <details><summary>Click to expand</summary>
+
+
+   We take Apollo2-7B or Apollo-MoE-0.5B as example
+   1. Download Dataset for project:
+
+      ```
+      bash 0.download_data.sh  
+      ```
+    
+   2. Prepare test and dev data for specific model:
+
+      
+      - Create test data for with special token
+        
+       ```
+       bash 1.data_process_test&dev.sh
+       ```
+    
+   3. Prepare train data for specific model (Create tokenized data in advance):
+
+    
+      - You can adjust data Training order and Training Epoch in this step
+
+       ```
+       bash 2.data_process_train.sh
+       ```
+    
+   4. Train the model
+
+    
+      - If you want to train in Multi Nodes please refer to ./src/sft/training_config/zero_multi.yaml
+
+
+       ```
+       bash 3.single_node_train.sh
+       ```
+
+
+   5. Evaluate your model: Generate score for benchmark
+      
+         ```
+         bash 4.eval.sh
+         ```
+
+   </details>
+
+
+
+##  Citation
+Please use the following citation if you intend to use our dataset for training or evaluation:
+
+```
+@misc{zheng2024efficientlydemocratizingmedicalllms,
+      title={Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts}, 
+      author={Guorui Zheng and Xidong Wang and Juhao Liang and Nuo Chen and Yuping Zheng and Benyou Wang},
+      year={2024},
+      eprint={2410.10626},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2410.10626}, 
+}
+```

SECURITY.md

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+<!-- BEGIN MICROSOFT SECURITY.MD V0.0.9 BLOCK -->
+
+## Security
+
+Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet) and [Xamarin](https://github.com/xamarin).
+
+If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://aka.ms/security.md/definition), please report it to us as described below.
+
+## Reporting Security Issues
+
+**Please do not report security vulnerabilities through public GitHub issues.**
+
+Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://aka.ms/security.md/msrc/create-report).
+
+If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com).  If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://aka.ms/security.md/msrc/pgp).
+
+You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://www.microsoft.com/msrc). 
+
+Please include the requested information listed below (as much as you can provide) to help us better understand the nature and scope of the possible issue:
+
+  * Type of issue (e.g. buffer overflow, SQL injection, cross-site scripting, etc.)
+  * Full paths of source file(s) related to the manifestation of the issue
+  * The location of the affected source code (tag/branch/commit or direct URL)
+  * Any special configuration required to reproduce the issue
+  * Step-by-step instructions to reproduce the issue
+  * Proof-of-concept or exploit code (if possible)
+  * Impact of the issue, including how an attacker might exploit the issue
+
+This information will help us triage your report more quickly.
+
+If you are reporting for a bug bounty, more complete reports can contribute to a higher bounty award. Please visit our [Microsoft Bug Bounty Program](https://aka.ms/security.md/msrc/bounty) page for more details about our active programs.
+
+## Preferred Languages
+
+We prefer all communications to be in English.
+
+## Policy
+
+Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://aka.ms/security.md/cvd).
+
+<!-- END MICROSOFT SECURITY.MD BLOCK -->

added_tokens.json

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+{
+  "<|assistant|>": 32001,
+  "<|endoftext|>": 32000,
+  "<|end|>": 32007,
+  "<|placeholder1|>": 32002,
+  "<|placeholder2|>": 32003,
+  "<|placeholder3|>": 32004,
+  "<|placeholder4|>": 32005,
+  "<|placeholder5|>": 32008,
+  "<|placeholder6|>": 32009,
+  "<|system|>": 32006,
+  "<|user|>": 32010
+}

config.json

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+{
+  "architectures": [
+    "Phi3ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_phi3.Phi3Config",
+    "AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM"
+  },
+  "bos_token_id": 1,
+  "embd_pdrop": 0.0,
+  "eos_token_id": 32000,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 4096,
+  "model_type": "phi3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "original_max_position_embeddings": 4096,
+  "pad_token_id": 32000,
+  "resid_pdrop": 0.0,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "sliding_window": 2047,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.42.3",
+  "use_cache": true,
+  "vocab_size": 32064
+}

configuration.json

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+{"framework": "pytorch", "task": "question-answering", "allow_remote": true}

configuration_phi3.py

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+# coding=utf-8
+# Copyright 2024 Microsoft and the 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.
+
+""" Phi-3 model configuration"""
+
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+PHI3_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "microsoft/Phi-3-mini-4k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/config.json",
+    "microsoft/Phi-3-mini-128k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/config.json",
+}
+
+
+class Phi3Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Phi3Model`]. It is used to instantiate a Phi-3
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the
+    [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct).
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32064):
+            Vocabulary size of the Phi-3 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`Phi3Model`].
+        hidden_size (`int`, *optional*, defaults to 3072):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 8192):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        resid_pdrop (`float`, *optional*, defaults to 0.0):
+            Dropout probability for mlp outputs.
+        embd_pdrop (`int`, *optional*, defaults to 0.0):
+            The dropout ratio for the embeddings.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio after computing the attention scores.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model might ever be used with.
+        original_max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model was trained with. This is used to determine the size of the
+            original RoPE embeddings when using long scaling.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon value used for the RMSNorm.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`dict`, *optional*):
+            The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
+            contain the following keys: `type`, `short_factor` and `long_factor`. The `type` must be either `su` or `yarn` and
+            the `short_factor` and `long_factor` must be lists of numbers with the same length as the hidden size
+            divided by the number of attention heads divided by 2.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 32000):
+            The id of the "end-of-sequence" token.
+        pad_token_id (`int`, *optional*, defaults to 32000):
+            The id of the padding token.
+        sliding_window (`int`, *optional*):
+            Sliding window attention window size. If `None`, no sliding window is applied.
+
+    Example:
+
+    ```python
+    >>> from transformers import Phi3Model, Phi3Config
+
+    >>> # Initializing a Phi-3 style configuration
+    >>> configuration = Phi3Config.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
+
+    >>> # Initializing a model from the configuration
+    >>> model = Phi3Model(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "phi3"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32064,
+        hidden_size=3072,
+        intermediate_size=8192,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        resid_pdrop=0.0,
+        embd_pdrop=0.0,
+        attention_dropout=0.0,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        original_max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        bos_token_id=1,
+        eos_token_id=32000,
+        pad_token_id=32000,
+        sliding_window=None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.resid_pdrop = resid_pdrop
+        self.embd_pdrop = embd_pdrop
+        self.attention_dropout = attention_dropout
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        self.original_max_position_embeddings = original_max_position_embeddings
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+        self.sliding_window = sliding_window
+
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            pad_token_id=pad_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 3:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
+        rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["su", "yarn"]:
+            raise ValueError(f"`rope_scaling`'s type field must be one of ['su', 'yarn'], got {rope_scaling_type}")
+        if not (
+            isinstance(rope_scaling_short_factor, list)
+            and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
+            )
+        if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2:
+            raise ValueError(
+                f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
+            )
+        if not (
+            isinstance(rope_scaling_long_factor, list)
+            and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
+            )
+        if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2:
+            raise ValueError(
+                f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
+            )

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": [
+    32000,
+    32001,
+    32007
+  ],
+  "pad_token_id": 32000,
+  "transformers_version": "4.42.3"
+}

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sample_finetune.py

@@ -0,0 +1,217 @@
+import sys
+import logging
+
+import datasets
+from datasets import load_dataset
+from peft import LoraConfig
+import torch
+import transformers
+from trl import SFTTrainer
+from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, BitsAndBytesConfig
+
+"""
+A simple example on using SFTTrainer and Accelerate to finetune Phi-3 models. For
+a more advanced example, please follow HF alignment-handbook/scripts/run_sft.py.
+This example has utilized DeepSpeed ZeRO3 offload to reduce the memory usage. The
+script can be run on V100 or later generation GPUs. Here are some suggestions on 
+futher reducing memory consumption:
+    - reduce batch size
+    - decrease lora dimension
+    - restrict lora target modules
+Please follow these steps to run the script:
+1. Install dependencies: 
+    conda install -c conda-forge accelerate
+    pip3 install -i https://pypi.org/simple/ bitsandbytes
+    pip3 install peft transformers trl datasets
+    pip3 install deepspeed
+2. Setup accelerate and deepspeed config based on the machine used:
+    accelerate config
+Here is a sample config for deepspeed zero3:
+    compute_environment: LOCAL_MACHINE
+    debug: false
+    deepspeed_config:
+      gradient_accumulation_steps: 1
+      offload_optimizer_device: none
+      offload_param_device: none
+      zero3_init_flag: true
+      zero3_save_16bit_model: true
+      zero_stage: 3
+    distributed_type: DEEPSPEED
+    downcast_bf16: 'no'
+    enable_cpu_affinity: false
+    machine_rank: 0
+    main_training_function: main
+    mixed_precision: bf16
+    num_machines: 1
+    num_processes: 4
+    rdzv_backend: static
+    same_network: true
+    tpu_env: []
+    tpu_use_cluster: false
+    tpu_use_sudo: false
+    use_cpu: false
+3. check accelerate config:
+    accelerate env
+4. Run the code:
+    accelerate launch sample_finetune.py
+"""
+
+logger = logging.getLogger(__name__)
+
+
+###################
+# Hyper-parameters
+###################
+training_config = {
+    "bf16": True,
+    "do_eval": False,
+    "learning_rate": 5.0e-06,
+    "log_level": "info",
+    "logging_steps": 20,
+    "logging_strategy": "steps",
+    "lr_scheduler_type": "cosine",
+    "num_train_epochs": 1,
+    "max_steps": -1,
+    "output_dir": "./checkpoint_dir",
+    "overwrite_output_dir": True,
+    "per_device_eval_batch_size": 4,
+    "per_device_train_batch_size": 4,
+    "remove_unused_columns": True,
+    "save_steps": 100,
+    "save_total_limit": 1,
+    "seed": 0,
+    "gradient_checkpointing": True,
+    "gradient_checkpointing_kwargs":{"use_reentrant": False},
+    "gradient_accumulation_steps": 1,
+    "warmup_ratio": 0.2,
+    }
+
+peft_config = {
+    "r": 16,
+    "lora_alpha": 32,
+    "lora_dropout": 0.05,
+    "bias": "none",
+    "task_type": "CAUSAL_LM",
+    "target_modules": "all-linear",
+    "modules_to_save": None,
+}
+train_conf = TrainingArguments(**training_config)
+peft_conf = LoraConfig(**peft_config)
+
+
+###############
+# Setup logging
+###############
+logging.basicConfig(
+    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+    datefmt="%Y-%m-%d %H:%M:%S",
+    handlers=[logging.StreamHandler(sys.stdout)],
+)
+log_level = train_conf.get_process_log_level()
+logger.setLevel(log_level)
+datasets.utils.logging.set_verbosity(log_level)
+transformers.utils.logging.set_verbosity(log_level)
+transformers.utils.logging.enable_default_handler()
+transformers.utils.logging.enable_explicit_format()
+
+# Log on each process a small summary
+logger.warning(
+    f"Process rank: {train_conf.local_rank}, device: {train_conf.device}, n_gpu: {train_conf.n_gpu}"
+    + f" distributed training: {bool(train_conf.local_rank != -1)}, 16-bits training: {train_conf.fp16}"
+)
+logger.info(f"Training/evaluation parameters {train_conf}")
+logger.info(f"PEFT parameters {peft_conf}")
+
+
+################
+# Modle Loading
+################
+checkpoint_path = "microsoft/Phi-3-mini-4k-instruct"
+# checkpoint_path = "microsoft/Phi-3-mini-128k-instruct"
+model_kwargs = dict(
+    use_cache=False,
+    trust_remote_code=True,
+    attn_implementation="flash_attention_2",  # loading the model with flash-attenstion support
+    torch_dtype=torch.bfloat16,
+    device_map=None
+)
+model = AutoModelForCausalLM.from_pretrained(checkpoint_path, **model_kwargs)
+tokenizer = AutoTokenizer.from_pretrained(checkpoint_path)
+tokenizer.model_max_length = 2048
+tokenizer.pad_token = tokenizer.unk_token  # use unk rather than eos token to prevent endless generation
+tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
+tokenizer.padding_side = 'right'
+
+
+##################
+# Data Processing
+##################
+def apply_chat_template(
+    example,
+    tokenizer,
+):
+    messages = example["messages"]
+    # Add an empty system message if there is none
+    if messages[0]["role"] != "system":
+        messages.insert(0, {"role": "system", "content": ""})
+    example["text"] = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=False)
+    return example
+
+raw_dataset = load_dataset("HuggingFaceH4/ultrachat_200k")
+train_dataset = raw_dataset["train_sft"]
+test_dataset = raw_dataset["test_sft"]
+column_names = list(train_dataset.features)
+
+processed_train_dataset = train_dataset.map(
+    apply_chat_template,
+    fn_kwargs={"tokenizer": tokenizer},
+    num_proc=10,
+    remove_columns=column_names,
+    desc="Applying chat template to train_sft",
+)
+
+processed_test_dataset = test_dataset.map(
+    apply_chat_template,
+    fn_kwargs={"tokenizer": tokenizer},
+    num_proc=10,
+    remove_columns=column_names,
+    desc="Applying chat template to test_sft",
+)
+
+
+###########
+# Training
+###########
+trainer = SFTTrainer(
+    model=model,
+    args=train_conf,
+    peft_config=peft_conf,
+    train_dataset=processed_train_dataset,
+    eval_dataset=processed_test_dataset,
+    max_seq_length=2048,
+    dataset_text_field="text",
+    tokenizer=tokenizer,
+    packing=True
+)
+train_result = trainer.train()
+metrics = train_result.metrics
+trainer.log_metrics("train", metrics)
+trainer.save_metrics("train", metrics)
+trainer.save_state()
+
+
+#############
+# Evaluation
+#############
+tokenizer.padding_side = 'left'
+metrics = trainer.evaluate()
+metrics["eval_samples"] = len(processed_test_dataset)
+trainer.log_metrics("eval", metrics)
+trainer.save_metrics("eval", metrics)
+
+
+# ############
+# # Save model
+# ############
+trainer.save_model(train_conf.output_dir)

special_tokens_map.json

@@ -0,0 +1,24 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<unk>",
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
+size 499723

tokenizer_config.json

@@ -0,0 +1,131 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "add_prefix_space": null,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": false
+    },
+    "32000": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32001": {
+      "content": "<|assistant|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32002": {
+      "content": "<|placeholder1|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32003": {
+      "content": "<|placeholder2|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32004": {
+      "content": "<|placeholder3|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32005": {
+      "content": "<|placeholder4|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32006": {
+      "content": "<|system|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32007": {
+      "content": "<|end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32008": {
+      "content": "<|placeholder5|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32009": {
+      "content": "<|placeholder6|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "32010": {
+      "content": "<|user|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "legacy": false,
+  "model_max_length": 4096,
+  "pad_token": "<unk>",
+  "padding_side": "left",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}