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CODE_OF_CONDUCT.md
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CODE_OF_CONDUCT.md
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# Microsoft Open Source Code of Conduct
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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
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Resources:
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- [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/)
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- [Microsoft Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/)
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- Contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with questions or concerns
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LICENSE
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LICENSE
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Microsoft.
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Copyright (c) Microsoft Corporation.
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MIT License
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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README.md
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---
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license: mit
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license_link: https://huggingface.co/microsoft/Phi-4-reasoning/resolve/main/LICENSE
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language:
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- en
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base_model:
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- microsoft/phi-4
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pipeline_tag: text-generation
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tags:
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- phi
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- nlp
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- math
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- code
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- chat
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- conversational
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- reasoning
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inference:
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parameters:
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temperature: 0
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widget:
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- messages:
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- role: user
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content: What is the derivative of x^2?
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library_name: transformers
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---
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# Phi-4-reasoning Model Card
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[Phi-4-reasoning Technical Report](https://huggingface.co/papers/2504.21318)
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## Model Summary
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| | |
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|-------------------------|-------------------------------------------------------------------------------|
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| **Developers** | Microsoft Research |
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| **Description** | Phi-4-reasoning is a state-of-the-art open-weight reasoning model finetuned from Phi-4 using supervised fine-tuning on a dataset of chain-of-thought traces and reinforcement learning. The supervised fine-tuning dataset includes a blend of synthetic prompts and high-quality filtered data from public domain websites, focused on math, science, and coding skills as well as alignment data for safety and Responsible AI. The goal of this approach was to ensure that small capable models were trained with data focused on high quality and advanced reasoning. |
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| **Architecture** | Base model same as previously released Phi-4, 14B parameters, dense decoder-only Transformer model |
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| **Inputs** | Text, best suited for prompts in the chat format |
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| **Context length** | 32k tokens |
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| **GPUs** | 32 H100-80G |
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| **Training time** | 2.5 days |
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| **Training data** | 16B tokens, ~8.3B unique tokens |
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| **Outputs** | Generated text in response to the input. Model responses have two sections, namely, a reasoning chain-of-thought block followed by a summarization block |
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| **Dates** | January 2025 – April 2025 |
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| **Status** | Static model trained on an offline dataset with cutoff dates of March 2025 and earlier for publicly available data |
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| **Release date** | April 30, 2025 |
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| **License** | MIT |
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## Intended Use
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| | |
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|-------------------------------|-------------------------------------------------------------------------|
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| **Primary Use Cases** | Our model is designed to accelerate research on language models, for use as a building block for generative AI powered features. It provides uses for general purpose AI systems and applications (primarily in English) which require:<br><br>1. Memory/compute constrained environments.<br>2. Latency bound scenarios.<br>3. Reasoning and logic. |
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| **Out-of-Scope Use Cases** | This model is designed and tested for math reasoning only. Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case, including the model’s focus on English. Review the Responsible AI Considerations section below for further guidance when choosing a use case. Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under. |
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## Usage
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> [!IMPORTANT]
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> To fully take advantage of the model's capabilities, inference must use `temperature=0.8`, `top_k=50`, `top_p=0.95`, and `do_sample=True`. For more complex queries, set `max_new_tokens=32768` to allow for longer chain-of-thought (CoT).
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### Input Formats
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Given the nature of the training data, **always use** ChatML template with the **following system prompt** for inference:
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```bash
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<|im_start|>system<|im_sep|>
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You are Phi, a language model trained by Microsoft to help users. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> {Thought section} </think> {Solution section}. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion. Now, try to solve the following question through the above guidelines:<|im_end|>
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<|im_start|>user<|im_sep|>
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What is the derivative of x^2?<|im_end|>
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<|im_start|>assistant<|im_sep|>
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```
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### With `transformers`
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-4-reasoning")
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model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-4-reasoning", device_map="auto", torch_dtype="auto")
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messages = [
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{"role": "system", "content": "You are Phi, a language model trained by Microsoft to help users. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> {Thought section} </think> {Solution section}. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion. Now, try to solve the following question through the above guidelines:"},
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{"role": "user", "content": "What is the derivative of x^2?"},
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]
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inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
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outputs = model.generate(
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inputs.to(model.device),
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max_new_tokens=4096,
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temperature=0.8,
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top_k=50,
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top_p=0.95,
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do_sample=True,
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)
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print(tokenizer.decode(outputs[0]))
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```
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### With `vllm`
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```bash
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vllm serve microsoft/Phi-4-reasoning --enable-reasoning --reasoning-parser deepseek_r1
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```
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*Phi-4-reasoning is also supported out-of-the-box by Ollama, llama.cpp, and any Phi-4 compatible framework.*
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## Data Overview
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### Training Datasets
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Our training data is a mixture of Q&A, chat format data in math, science, and coding. The chat prompts are sourced from filtered high-quality web data and optionally rewritten and processed through a synthetic data generation pipeline. We further include data to improve truthfulness and safety.
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### Benchmark Datasets
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We evaluated Phi-4-reasoning using the open-source [Eureka](https://github.com/microsoft/eureka-ml-insights) evaluation suite and our own internal benchmarks to understand the model's capabilities. More specifically, we evaluate our model on:
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Reasoning tasks:
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* **AIME 2025, 2024, 2023, and 2022:** Math olympiad questions.
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* **GPQA-Diamond:** Complex, graduate-level science questions.
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* **OmniMath:** Collection of over 4000 olympiad-level math problems with human annotation.
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* **LiveCodeBench:** Code generation benchmark gathered from competitive coding contests.
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* **3SAT (3-literal Satisfiability Problem) and TSP (Traveling Salesman Problem):** Algorithmic problem solving.
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* **BA Calendar:** Planning.
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* **Maze and SpatialMap:** Spatial understanding.
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General-purpose benchmarks:
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* **Kitab:** Information retrieval.
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* **IFEval and ArenaHard:** Instruction following.
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* **PhiBench:** Internal benchmark.
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* **FlenQA:** Impact of prompt length on model performance.
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* **HumanEvalPlus:** Functional code generation.
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* **MMLU-Pro:** Popular aggregated dataset for multitask language understanding.
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## Safety
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### Approach
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Phi-4-reasoning has adopted a robust safety post-training approach via supervised fine-tuning (SFT). This approach leverages a variety of both open-source and in-house generated synthetic prompts, with LLM-generated responses that adhere to rigorous Microsoft safety guidelines, e.g., User Understanding and Clarity, Security and Ethical Guidelines, Limitations, Disclaimers and Knowledge Scope, Handling Complex and Sensitive Topics, Safety and Respectful Engagement, Confidentiality of Guidelines and Confidentiality of Chain-of-Thoughts.
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### Safety Evaluation and Red-Teaming
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Prior to release, Phi-4-reasoning followed a multi-faceted evaluation approach. Quantitative evaluation was conducted with multiple open-source safety benchmarks and in-house tools utilizing adversarial conversation simulation. For qualitative safety evaluation, we collaborated with the independent AI Red Team (AIRT) at Microsoft to assess safety risks posed by Phi-4-reasoning in both average and adversarial user scenarios. In the average user scenario, AIRT emulated typical single-turn and multi-turn interactions to identify potentially risky behaviors. The adversarial user scenario tested a wide range of techniques aimed at intentionally subverting the model's safety training including grounded-ness, jailbreaks, harmful content like hate and unfairness, violence, sexual content, or self-harm, and copyright violations for protected material. We further evaluate models on Toxigen, a benchmark designed to measure bias and toxicity targeted towards minority groups.
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Please refer to the technical report for more details on safety alignment.
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## Model Quality
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At the high-level overview of the model quality on representative benchmarks. For the tables below, higher numbers indicate better performance:
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| | AIME 24 | AIME 25 | OmniMath | GPQA-D | LiveCodeBench (8/1/24–2/1/25) |
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|-----------------------------|-------------|-------------|-------------|------------|-------------------------------|
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| Phi-4-reasoning | 75.3 | 62.9 | 76.6 | 65.8 | 53.8 |
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| Phi-4-reasoning-plus | 81.3 | 78.0 | 81.9 | 68.9 | 53.1 |
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| OpenThinker2-32B | 58.0 | 58.0 | — | 64.1 | — |
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| QwQ 32B | 79.5 | 65.8 | — | 59.5 | 63.4 |
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| EXAONE-Deep-32B | 72.1 | 65.8 | — | 66.1 | 59.5 |
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| DeepSeek-R1-Distill-70B | 69.3 | 51.5 | 63.4 | 66.2 | 57.5 |
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| DeepSeek-R1 | 78.7 | 70.4 | 85.0 | 73.0 | 62.8 |
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| o1-mini | 63.6 | 54.8 | — | 60.0 | 53.8 |
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| o1 | 74.6 | 75.3 | 67.5 | 76.7 | 71.0 |
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| o3-mini | 88.0 | 78.0 | 74.6 | 77.7 | 69.5 |
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| Claude-3.7-Sonnet | 55.3 | 58.7 | 54.6 | 76.8 | — |
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| Gemini-2.5-Pro | 92.0 | 86.7 | 61.1 | 84.0 | 69.2 |
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| | Phi-4 | Phi-4-reasoning | Phi-4-reasoning-plus | o3-mini | GPT-4o |
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|----------------------------------------|-------|------------------|-------------------|---------|--------|
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| FlenQA [3K-token subset] | 82.0 | 97.7 | 97.9 | 96.8 | 90.8 |
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| IFEval Strict | 62.3 | 83.4 | 84.9 | 91.5 | 81.8 |
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| ArenaHard | 68.1 | 73.3 | 79.0 | 81.9 | 75.6 |
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| HumanEvalPlus | 83.5 | 92.9 | 92.3 | 94.0| 88.0 |
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| MMLUPro | 71.5 | 74.3 | 76.0 | 79.4 | 73.0 |
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| Kitab<br><small>No Context - Precision<br>With Context - Precision<br>No Context - Recall<br>With Context - Recall</small> | <br>19.3<br>88.5<br>8.2<br>68.1 | <br>23.2<br>91.5<br>4.9<br>74.8 | <br>27.6<br>93.6<br>6.3<br>75.4 | <br>37.9<br>94.0<br>4.2<br>76.1 | <br>53.7<br>84.7<br>20.3<br>69.2 |
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| Toxigen Discriminative<br><small>Toxic category<br>Neutral category</small> | <br>72.6<br>90.0 | <br>86.7<br>84.7 | <br>77.3<br>90.5 | <br>85.4<br>88.7 | <br>87.6<br>85.1 |
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| PhiBench 2.21 | 58.2 | 70.6 | 74.2 | 78.0| 72.4 |
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Overall, Phi-4-reasoning, with only 14B parameters, performs well across a wide range of reasoning tasks, outperforming significantly larger open-weight models such as DeepSeek-R1 distilled 70B model and approaching the performance levels of full DeepSeek R1 model. We also test the models on multiple new reasoning benchmarks for algorithmic problem solving and planning, including 3SAT, TSP, and BA-Calendar. These new tasks are nominally out-of-domain for the models as the training process did not intentionally target these skills, but the models still show strong generalization to these tasks. Furthermore, when evaluating performance against standard general abilities benchmarks such as instruction following or non-reasoning tasks, we find that our new models improve significantly from Phi-4, despite the post-training being focused on reasoning skills in specific domains.
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## Responsible AI Considerations
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Like other language models, Phi-4-reasoning can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include:
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* **Quality of Service:** The model is trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English. Phi-4-reasoning is not intended to support multilingual use.
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* **Representation of Harms & Perpetuation of Stereotypes:** These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases.
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* **Inappropriate or Offensive Content:** These models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case.
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* **Information Reliability:** Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated.
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* **Election Information Reliability:** The model has an elevated defect rate when responding to election-critical queries, which may result in incorrect or unauthoritative election critical information being presented. We are working to improve the model's performance in this area. Users should verify information related to elections with the election authority in their region.
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* **Limited Scope for Code:** Majority of Phi-4-reasoning training data is based in Python and uses common packages such as `typing`, `math`, `random`, `collections`, `datetime`, `itertools`. If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses.
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Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Using safety services like [Azure AI Content Safety](https://azure.microsoft.com/en-us/products/ai-services/ai-content-safety) that have advanced guardrails is highly recommended. Important areas for consideration include:
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* **Allocation:** Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques.
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* **High-Risk Scenarios:** Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context.
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* **Misinformation:** Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG).
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* **Generation of Harmful Content:** Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case.
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* **Misuse:** Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations.
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||||
* **Data Summary:** https://huggingface.co/microsoft/Phi-4-reasoning/blob/main/data_summary_card.md
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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 -->
|
||||
32
config.json
Normal file
32
config.json
Normal file
@@ -0,0 +1,32 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Phi3ForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
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|
||||
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||||
"hidden_act": "silu",
|
||||
"hidden_size": 5120,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 17920,
|
||||
"max_position_embeddings": 32768,
|
||||
"model_type": "phi3",
|
||||
"num_attention_heads": 40,
|
||||
"num_hidden_layers": 40,
|
||||
"num_key_value_heads": 10,
|
||||
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|
||||
"pad_token_id": 100349,
|
||||
"partial_rotary_factor": 1.0,
|
||||
"resid_pdrop": 0.0,
|
||||
"rms_norm_eps": 1e-05,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 500000,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.51.1",
|
||||
"use_cache": true,
|
||||
"vocab_size": 100352
|
||||
}
|
||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
||||
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||
149
data_summary_card.md
Normal file
149
data_summary_card.md
Normal file
@@ -0,0 +1,149 @@
|
||||
|
||||
|
||||
# Data Summary for microsoft_Phi-4-reasoning
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## 1. General information
|
||||
|
||||
**1.0.1 Version of the Summary:** 1.0
|
||||
|
||||
|
||||
|
||||
**1.0.2 Last update:** 24-Nov-2025
|
||||
|
||||
|
||||
|
||||
## 1.1 Model Developer Identification
|
||||
|
||||
**1.1.1 Model Developer name and contact details:** Microsoft Corporation at One Microsoft Way, Redmond, WA 98052. Tel: 425-882-8080
|
||||
|
||||
|
||||
|
||||
## 1.2 Model Identification
|
||||
|
||||
**1.2.1 Versioned model name(s):** Phi-4-reasoning
|
||||
|
||||
|
||||
|
||||
**1.2.2 Model release date:** 30-Apr-2025
|
||||
|
||||
|
||||
|
||||
## 1.3 Overall training data size and characteristics
|
||||
|
||||
### 1.3.1 Size of dataset and characteristics
|
||||
|
||||
**1.3.1.A Text training data size:** 1 billion to 1 trillion tokens
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
**1.3.1.B Text training data content:** Prompts sourced from publicly available websites, existing datasets, and licensed collections, augmented with synthetically generated problems; responses generated using o3-mini including chain-of-thought traces; includes STEM, coding, logical puzzles, and safety/Responsible AI alignment data
|
||||
|
||||
|
||||
|
||||
**1.3.1.C Image training data size:** Not applicable. Images are not part of the training data
|
||||
|
||||
|
||||
|
||||
**1.3.1.D Image training data content:** Not applicable
|
||||
|
||||
|
||||
|
||||
**1.3.1.E Audio training data size:** Not applicable. Audio data is not part of the training data
|
||||
|
||||
|
||||
|
||||
**1.3.1.F Audio training data content:** Not applicable
|
||||
|
||||
|
||||
|
||||
**1.3.1.G Video training data size:** Not applicable. Video data is not part of the training data
|
||||
|
||||
|
||||
|
||||
**1.3.1.H Video training data content:** Not applicable
|
||||
|
||||
|
||||
|
||||
**1.3.1.I Other training data size:** Not applicable
|
||||
|
||||
|
||||
|
||||
**1.3.1.J Other training data content:** Not applicable
|
||||
|
||||
|
||||
|
||||
**1.3.2 Latest date of data acquisition/collection for model training:** 31-Mar-2025
|
||||
|
||||
|
||||
|
||||
**1.3.3 Is data collection ongoing to update the model with new data collection after deployment?** No
|
||||
|
||||
|
||||
|
||||
**1.3.4 Date the training dataset was first used to train the model:** 01-Jan-2025
|
||||
|
||||
|
||||
|
||||
**1.3.5 Rationale or purpose of data selection:** Datasets were curated to emphasize complex multi-step reasoning and verifiable solutions across STEM, coding, and safety, selecting prompts at the boundary of base model capabilities. Synthetic problems and teacher-generated reasoning traces were used to distill structured chain-of-thought and promote concise, checkable answers, supporting robust reasoning performance and generalization to broader tasks
|
||||
|
||||
|
||||
|
||||
## 2. List of data sources
|
||||
|
||||
### 2.1 Publicly available datasets
|
||||
|
||||
**2.1.1 Have you used publicly available datasets to train the model?** Yes
|
||||
|
||||
|
||||
|
||||
## 2.2 Private non-publicly available datasets obtained from third parties
|
||||
|
||||
### 2.2.1 Datasets commercially licensed by rights holders or their representatives
|
||||
|
||||
**2.2.1.A Have you concluded transactional commercial licensing agreement(s) with rights holder(s) or with their representatives?** Yes
|
||||
|
||||
|
||||
|
||||
### 2.2.2 Private datasets obtained from other third-parties
|
||||
|
||||
**2.2.2.A Have you obtained private datasets from third parties that are not licensed as described in Section 2.2.1, such as data obtained from providers of private databases, or data intermediaries?** This information cannot be provided due to unavailability of the underlying data (e.g., loss, corruption, or other access limitations)
|
||||
|
||||
|
||||
|
||||
## 2.3 Personal Information
|
||||
|
||||
**2.3.1 Was personal data used to train the model?** Microsoft follows all relevant laws and regulations pertaining to personal information
|
||||
|
||||
|
||||
|
||||
## 2.4 Synthetic data
|
||||
|
||||
**2.4.1 Was any synthetic AI-generated data used to train the model?** Yes
|
||||
|
||||
|
||||
|
||||
## 3. Data processing aspects
|
||||
|
||||
### 3.1 Respect of reservation of rights from text and data mining exception or limitation
|
||||
|
||||
**3.1.1 Does this dataset include any data protected by copyright, trademark, or patent?** Microsoft follows all required regulations and laws for processing data protected by copyright, trademark, or patent
|
||||
|
||||
|
||||
|
||||
## 3.2 Other information
|
||||
|
||||
**3.2.1 Does the dataset include information about consumer groups without revealing individual consumer identities?** Microsoft follows all required regulations and laws for protecting consumer identities
|
||||
|
||||
|
||||
|
||||
**3.2.2 Was the dataset cleaned or modified before model training?** Yes
|
||||
|
||||
|
||||
|
||||
|
||||
11
generation_config.json
Normal file
11
generation_config.json
Normal file
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||||
{
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"top_k": 50,
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"top_p": 0.95,
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"transformers_version": "4.51.1"
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||||
}
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|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"100261": {
|
||||
"content": "<|dummy_1|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100262": {
|
||||
"content": "<|dummy_2|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100263": {
|
||||
"content": "<|dummy_3|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100264": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100265": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100266": {
|
||||
"content": "<|im_sep|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100267": {
|
||||
"content": "<|dummy_4|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100268": {
|
||||
"content": "<|dummy_5|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100269": {
|
||||
"content": "<|dummy_6|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100270": {
|
||||
"content": "<|dummy_7|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100271": {
|
||||
"content": "<|dummy_8|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100272": {
|
||||
"content": "<|dummy_9|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100273": {
|
||||
"content": "<|dummy_10|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100274": {
|
||||
"content": "<|dummy_11|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100275": {
|
||||
"content": "<|dummy_12|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100276": {
|
||||
"content": "<|endofprompt|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100277": {
|
||||
"content": "<|dummy_13|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100278": {
|
||||
"content": "<|dummy_14|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100279": {
|
||||
"content": "<|dummy_15|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100280": {
|
||||
"content": "<|dummy_16|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100281": {
|
||||
"content": "<|dummy_17|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100282": {
|
||||
"content": "<|dummy_18|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100283": {
|
||||
"content": "<|dummy_19|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100284": {
|
||||
"content": "<|dummy_20|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100285": {
|
||||
"content": "<|dummy_21|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100286": {
|
||||
"content": "<|dummy_22|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100287": {
|
||||
"content": "<|dummy_23|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100288": {
|
||||
"content": "<|dummy_24|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100289": {
|
||||
"content": "<|dummy_25|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100290": {
|
||||
"content": "<|dummy_26|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100291": {
|
||||
"content": "<|dummy_27|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100292": {
|
||||
"content": "<|dummy_28|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100293": {
|
||||
"content": "<|dummy_29|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100294": {
|
||||
"content": "<|dummy_30|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100295": {
|
||||
"content": "<|dummy_31|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100296": {
|
||||
"content": "<|dummy_32|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100297": {
|
||||
"content": "<|dummy_33|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100298": {
|
||||
"content": "<|dummy_34|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100299": {
|
||||
"content": "<|dummy_35|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100300": {
|
||||
"content": "<|dummy_36|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100301": {
|
||||
"content": "<|dummy_37|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100302": {
|
||||
"content": "<|dummy_38|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100303": {
|
||||
"content": "<|dummy_39|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100304": {
|
||||
"content": "<|dummy_40|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100305": {
|
||||
"content": "<|dummy_41|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100306": {
|
||||
"content": "<|dummy_42|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100307": {
|
||||
"content": "<|dummy_43|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100308": {
|
||||
"content": "<|dummy_44|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100309": {
|
||||
"content": "<|dummy_45|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100310": {
|
||||
"content": "<|dummy_46|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100311": {
|
||||
"content": "<|dummy_47|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100312": {
|
||||
"content": "<|dummy_48|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100313": {
|
||||
"content": "<|dummy_49|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100314": {
|
||||
"content": "<|dummy_50|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100315": {
|
||||
"content": "<|dummy_51|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100316": {
|
||||
"content": "<|dummy_52|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100317": {
|
||||
"content": "<|dummy_53|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100318": {
|
||||
"content": "<|dummy_54|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100319": {
|
||||
"content": "<|dummy_55|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100320": {
|
||||
"content": "<|dummy_56|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100321": {
|
||||
"content": "<|dummy_57|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100322": {
|
||||
"content": "<|dummy_58|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100323": {
|
||||
"content": "<|dummy_59|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100324": {
|
||||
"content": "<|dummy_60|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100325": {
|
||||
"content": "<|dummy_61|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100326": {
|
||||
"content": "<|dummy_62|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100327": {
|
||||
"content": "<|dummy_63|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100328": {
|
||||
"content": "<|dummy_64|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100329": {
|
||||
"content": "<|dummy_65|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100330": {
|
||||
"content": "<|dummy_66|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100331": {
|
||||
"content": "<|dummy_67|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100332": {
|
||||
"content": "<|dummy_68|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100333": {
|
||||
"content": "<|dummy_69|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100334": {
|
||||
"content": "<|dummy_70|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100335": {
|
||||
"content": "<|dummy_71|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100336": {
|
||||
"content": "<|dummy_72|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100337": {
|
||||
"content": "<|dummy_73|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100338": {
|
||||
"content": "<|dummy_74|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100339": {
|
||||
"content": "<|dummy_75|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100340": {
|
||||
"content": "<|dummy_76|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100341": {
|
||||
"content": "<|dummy_77|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100342": {
|
||||
"content": "<|dummy_78|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100343": {
|
||||
"content": "<|dummy_79|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100344": {
|
||||
"content": "<|dummy_80|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100345": {
|
||||
"content": "<|dummy_81|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100346": {
|
||||
"content": "<|dummy_82|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100347": {
|
||||
"content": "<|dummy_83|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100348": {
|
||||
"content": "<|dummy_84|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100349": {
|
||||
"content": "<|dummy_85|>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100350": {
|
||||
"content": "<think>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"100351": {
|
||||
"content": "</think>",
|
||||
"lstrip": true,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"bos_token": "<|endoftext|>",
|
||||
"chat_template": "<|im_start|>system<|im_sep|>You are Phi, a language model trained by Microsoft to help users. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> {Thought section} </think> {Solution section}. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion. Now, try to solve the following question through the above guidelines:<|im_end|>{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|im_start|>user<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'assistant') %}{{'<|im_start|>assistant<|im_sep|>'}}{% generation %}{{message['content'] + '<|im_end|>'}}{% endgeneration %}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant<|im_sep|>' }}{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 32768,
|
||||
"pad_token": "<|dummy_85|>",
|
||||
"padding_side": "left",
|
||||
"tokenizer_class": "GPT2Tokenizer"
|
||||
}
|
||||
3
vocab.json
Normal file
3
vocab.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:23b80b3c4932a7733eb91487cb2fdbc56c5bd199a459128f538095a36dad5287
|
||||
size 1612628
|
||||
Reference in New Issue
Block a user