Create README.md

README.md

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+---
+license: apache-2.0
+datasets:
+- avemio/GRAG-CPT-HESSIAN-AI
+language:
+- en
+- de
+base_model:
+- ThomasComics/Phi-3-mini-128k-instruct-LLaMAfied
+pipeline_tag: question-answering
+tags:
+- German
+- RAG
+- Retrieval
+- Question-Answering
+- Summarization
+- Reasoning
+---
+
+
+<img src="https://www.grag.ai/wp-content/uploads/2024/12/GRAG-ICON-TO-WORDLOGO-Animation_Loop-small-ezgif.com-video-to-gif-converter.gif" alt="GRAG Logo" width="400" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
+
+
+# GRAG-PHI-3-mini-4B-CPT-HESSIAN-AI
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+**GRAG** (**G**erman **R**etrieval **A**ugmented **G**eneration) models are designed for the German-speaking market, enabling innovation and AI solutions to drive German research collaboration in business-focused Generative AI by 2025
+
+Our GRAG-MISTRAL-CPT model are trained on this **[GRAG-CPT](https://huggingface.co/datasets/avemio/GRAG-CPT-HESSIAN-AI) dataset.**
+
+## Model Details
+
+The core models released in this batch are the following: 
+| Size | Training Tokens | 
+|------|--------|
+| [GRAG-PHI-CPT](https://huggingface.co/avemio/GRAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI)   | 507.47 million |
+| [GRAG-PHI-SFT](https://huggingface.co/avemio/GRAG-PHI-3.5-MINI-4B-SFT-HESSIAN-AI) |  2.03 billion  |  
+| [GRAG-PHI-ORPO](https://huggingface.co/avemio/GRAG-PHI-3.5-MINI-4B-ORPO-HESSIAN-AI) |  2.0577 billion  | 
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **Developed by:** Avemio AI Team
+- **Supported by:** Hessian AI
+- **Model type:** a Transformer style autoregressive language model.
+- **Language(s) (NLP):** German, English
+- **License:** The code and model are released under Apache 2.0.
+- **Contact:** [grag@avemio.digital](mailto:grag@avemio.digital)
+
+
+### Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Project Page:**
+- **Repositories:** 
+    - Training: 
+    - Evaluation code: 
+- **Technical blog post:** 
+<!-- - **Press release:** TODO -->
+
+## 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. -->
+
+### Inference
+Quickly get inference running with the following required installation:
+Now, proceed as usual with HuggingFace:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+ 
+model_name = "avemio/GRAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI"
+ 
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+model = AutoModelForCausalLM.from_pretrained(model_name)
+inputs = tokenizer("Hello mein Name ist", return_tensors="pt")
+
+outputs = model.generate(**inputs, max_new_tokens=20)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+
+### Fine-tuning
+We are providing a comprehensive Google Colab notebook to guide users through the process of fine-tuning our model, complete with detailed instructions, essential dependencies, and configurable settings.
+ [Colab-Notebook](https://colab.research.google.com/drive/1U6aP3vIkABaCm7doGV1waHgTLvXNGbBp?usp=sharing).
+
+
+
+## Model Details
+
+### Data
+For training data details, please see the [GRAG-CPT-Dataset](https://huggingface.co/datasets/avemio/GRAG-CPT-HESSIAN-AI) documentation.
+
+#### Description
+The SFT tasks represent a focused approach to enhance model capabilities through specialized RAG examples. Most of these tasks were developed using synthetically enhanced data derived from the German Wikipedia, accessed through Cohere's prepared dataset on HuggingFace (licensed CC-BY-SA 4.0). This data was structured in a training knowledge graph where Question-Answer nodes were connected to both relevant and irrelevant Context nodes from the same Wikipedia page, creating a rich and challenging network of relationships for training. The only exceptions are the function calling dataset, which was derived and extended from Salesforce's XLAM Function calling dataset by including function call results and final answer generation, and the reasoning task which synthetic generation was inspired by the Paper from Tencent ([“Scaling Synthetic Data Creation with 1,000,000,000 Personas”](https://arxiv.org/abs/2406.20094)), to generate a diverse set of reasoning tasks across various domains.
+This comprehensive set of SFT tasks ensures the model develops robust capabilities across a wide range of practical applications while maintaining consistent output formats and clear communication patterns. Each task type has been carefully designed to address specific business needs while maintaining high standards of accuracy and reliability, making them valuable tools for organizations looking to enhance their information processing and knowledge management capabilities.
+
+### Architecture
+
+
+| Parameter            | GRAG-MISTRAL-CPT                                                                                   |
+|-----------------------|-----------------------------------------------------------------------------------------------|
+| **d_model**          | 4096                                                                                          |
+| **num heads**        | 32                                                                                            |
+| **num layers**       | 32                                                                                            |
+| **MLP ratio**        | 3.5                                                                                          |
+| **LayerNorm type**   | RMSNorm                                                                                       |
+| **pos embeddings**   | RoPE                                                                                          |
+| **attention variant**| Multi-head attention with 32 key-value heads                              |
+| **biases**           | none                                                                                          |
+| **block type**       | Sequential                                                                                    |
+| **activation**       | SiLU                                                                                          |
+| **sequence length**  | 131072                                                                                        |
+| **weight typing**     | bfloat16  
+
+### Hyperparameters 
+
+
+| Parameter                 | GRAG-MISTRAL-CPT       |
+|---------------------------|--------------------|
+| **warmup steps**          | 50                |
+| **peak LR**               | 5.0E-07           |
+| **weight decay**          | 0.1               |
+| **LR schedule**           | linear            |
+| **gradient reduce dtype** | FP32              |
+| **optimizer state dtype** | FP32              |
+
+## Environmental Impact
+
+GRAG-PHI-CPT, running on NVIDIA A100 with x GPUs for x days, has an approximate power consumption as follows:
+
+It's important to note that the actual power consumption may vary depending on the specific workload and operational conditions. For accurate power consumption measurements, using dedicated power monitoring tools is recommended.
+
+| Model          | GPU Type            | Power Consumption From GPUs | 
+|----------------|---------------------|-----------------------------|
+| GRAG-PHI-CPT   | A100 ([Hessian AI supercomputer](https://hessian.ai/de/)) | 0.xxx MWh                    |
+## Bias, Risks, and Limitations
+
+Like any base language model or fine-tuned model without safety filtering, it is relatively easy for a user to prompt these models to generate harmful and generally sensitive content.
+Such content can also be produced unintentionally, especially in the case of bias, so we recommend users consider the risks of applications of this technology.
+
+Otherwise, many facts from GRAG-MISTRAL-CPT or any LLM will often not be true, so they should be checked.
+
+
+
+
+## Model Card Contact
+
+
+For errors in this model card, please contact ([grag@avemio.digital](mailto:grag@avemio.digital)).