Update model card

README.md

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 ---
-library_name: transformers
-tags: []
+license: other
+license_name: msrla
+license_link: LICENSE
 ---
+# Phi-4 Dynamic fp8
 
-# Model Card for Model ID
+Quantized using [LLM Compressor's dynamic fp8 quanting](https://github.com/vllm-project/llm-compressor) on `NyxKrage/Microsoft_Phi-4`.
 
-<!-- Provide a quick summary of what the model is/does. -->
+Typically, this meaningfully increases inference speeds, e.g. for 500 token generations:
+- 17 output tok/s on an A40 in bf16
+- 32 output tok/s on an A40 in fp8
 
+BEWARE the license is a Microsoft Research one.
 
+vLLM compatible model that will run in:
+- 8bit weights and activations on Ada Lovelace or Hopper GPUs (e.g. RTX4090 or H100).
+- 8bit weights and 16 bit activations on Ampere GPUs (e.g. A40, A100), using Marlin kernels.
 
-## Model Details
+[One click Runpod template](https://runpod.io/console/deploy?template=rzgcdh9rqe&ref=jmfkcdio) (affiliate link).
+Other templates available from [Trelis' one-click-llms repo](https://github.com/TrelisResearch/one-click-llms).
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+---
+# Phi-4
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+Phi-4 is a state-of-the-art open model built upon a blend of synthetic datasets, data from filtered public domain websites, and acquired academic books and Q&A datasets. The goal of this approach was to ensure that small capable models were trained with data focused on high quality and advanced reasoning.
 
-## How to Get Started with the Model
+Phi-4 underwent a rigorous enhancement and alignment process, incorporating both supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures.
 
-Use the code below to get started with the model.
+For more information, reference the [Phi-4 Technical Report](https://www.microsoft.com/en-us/research/uploads/prod/2024/12/P4TechReport.pdf).
 
-[More Information Needed]
+### Model Architecture
 
-## Training Details
+Phi-4 is a 14B parameters, dense decoder-only transformer model.
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+Our training data is an extension of the data used for Phi-3 and includes a wide variety of sources from:
 
-## Environmental Impact
+1.  Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code.
+    
+2.  Newly created synthetic, "textbook-like" data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.).
+    
+3.  Acquired academic books and Q&A datasets.
+    
+4.  High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness.
+    
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+Multilingual data constitutes about 8% of our overall data. We are focusing on the quality of data that could potentially improve the reasoning ability for the model, and we filter the publicly available documents to contain the correct level of knowledge.
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+Intended Use
+------------
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+### Primary Use Cases
 
-## Technical Specifications [optional]
+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:
 
-### Model Architecture and Objective
+1.  Memory/compute constrained environments.
+2.  Latency bound scenarios.
+3.  Reasoning and logic.
 
-[More Information Needed]
+### Out-of-Scope Use Cases
 
-### Compute Infrastructure
+Our models is not specifically designed or evaluated for all downstream purposes, thus:
 
-[More Information Needed]
+1.  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.
+2.  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.
+3.  Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.
 
-#### Hardware
+Safety
+------
 
-[More Information Needed]
+### Approach
 
-#### Software
+Phi-4 has adopted a robust safety post-training approach. This approach leverages a variety of both open-source and in-house generated synthetic datasets. The overall technique employed to do the safety alignment is a combination of SFT (Supervised Fine-Tuning) and iterative DPO (Direct Preference Optimization), including publicly available datasets focusing on helpfulness and harmlessness as well as various questions and answers targeted to multiple safety categories.
 
-[More Information Needed]
+### Safety Evaluation and Red-Teaming
 
-## Citation [optional]
+Prior to release, Phi-4 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` 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 jailbreaks, encoding-based attacks, multi-turn attacks, and adversarial suffix attacks.
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+Please refer to the technical report for more details on safety alignment.
 
-**BibTeX:**
+Responsible AI Considerations
+-----------------------------
 
-[More Information Needed]
+Like other language models, `phi-4` can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include:
 
-**APA:**
+*   **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` is not intended to support multilingual use.
+    
+*   **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.
+    
+*   **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.
+    
+*   **Information Reliability:** Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated.
+    
+*   **Limited Scope for Code:** Majority of `phi-4` 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.
+    
 
-[More Information Needed]
+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:
 
-## Glossary [optional]
+*   **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.
+    
+*   **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.
+    
+*   **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).
+    
+*   **Generation of Harmful Content:** Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case.
+    
+*   **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.
+    
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+We evaluated `phi-4` using [OpenAI’s SimpleEval](https://github.com/openai/simple-evals) and our own internal benchmarks to understand the model’s capabilities, more specifically:
 
-[More Information Needed]
+*   **MMLU:** Popular aggregated dataset for multitask language understanding.
+    
+*   **MATH:** Challenging competition math problems.
+    
+*   **GPQA:** Complex, graduate-level science questions.
+    
+*   **DROP:** Complex comprehension and reasoning.
+    
+*   **MGSM:** Multi-lingual grade-school math.
+    
+*   **HumanEval:** Functional code generation.
+    
+*   **SimpleQA:** Factual responses.
+    
 
-## More Information [optional]
+To understand the capabilities, we compare `phi-4` with a set of models over OpenAI’s SimpleEval benchmark.
 
-[More Information Needed]
+At the high-level overview of the model quality on representative benchmarks. For the table below, higher numbers indicate better performance:
 
-## Model Card Authors [optional]
 
-[More Information Needed]
+| **Category**                 | **Benchmark**  | **phi-4** (14B)    | **phi-3** (14B) | **Qwen 2.5** (14B instruct) | **GPT-4o-mini** | **Llama-3.3** (70B instruct) | **Qwen 2.5** (72B instruct) | **GPT-4o**         |
+| ---------------------------- | -------------- | ------------------ | --------------- | --------------------------- | --------------- | ---------------------------- | --------------------------- | ------------------ |
+| Popular Aggregated Benchmark | MMLU           | 84.8               | 77.9            | 79.9                        | 81.8            | 86.3                         | 85.3                        | **88.1**           |
+| Science                      | GPQA           | **56.1**           | 31.2            | 42.9                        | 40.9            | 49.1                         | 49.0                        | 50.6               |
+| Math                         | MGSM  <br>MATH | 80.6  <br>**80.4** | 53.5  <br>44.6  | 79.6  <br>75.6              | 86.5  <br>73.0  | 89.1  <br>66.3*              | 87.3  <br>80.0              | **90.4**  <br>74.6 |
+| Code Generation              | HumanEval      | 82.6               | 67.8            | 72.1                        | 86.2            | 78.9*                        | 80.4                        | **90.6**           |
+| Factual Knowledge            | SimpleQA       | 3.0                | 7.6             | 5.4                         | 9.9             | 20.9                         | 10.2                        | **39.4**           |
+| Reasoning                    | DROP           | 75.5               | 68.3            | 85.5                        | 79.3            | **90.2**                     | 76.7                        | 80.9               |
 
-## Model Card Contact
+\* These scores are lower than those reported by Meta, perhaps because simple-evals has a strict formatting requirement that Llama models have particular trouble following. We use the simple-evals framework because it is reproducible, but Meta reports 77 for MATH and 88 for HumanEval on Llama-3.3-70B.
 
-[More Information Needed]