transformers safetensors llama text-generation German RAG Retrieval Question-Answering Summarization Reasoning question-answering en de base_model:avemio/German-RAG-PHI-3.5-MINI-4B-ORPO-HESSIAN-AI base_model:merge:avemio/German-RAG-PHI-3.5-MINI-4B-ORPO-HESSIAN-AI base_model:avemio/German-RAG-PHI-3.5-MINI-4B-SFT-HESSIAN-AI base_model:merge:avemio/German-RAG-PHI-3.5-MINI-4B-SFT-HESSIAN-AI license:mit autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model:
- avemio/German-RAG-PHI-3.5-MINI-4B-SFT-HESSIAN-AI
- avemio/German-RAG-PHI-3.5-MINI-4B-ORPO-HESSIAN-AI
language:
- en
- de
library_name: transformers
license: mit
pipeline_tag: question-answering
tags:
- German
- RAG
- Retrieval
- Question-Answering
- Summarization
- Reasoning
base_model_relation: merge
---

# German-RAG-PHI-3.5-MINI-4B-MERGED-HESSIAN-AI

<!-- Provide a quick summary of what the model is/does. -->

**German-RAG** (**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


## Model Details

The core models released in this batch are the following: 
| Size | Training Tokens | 
|------|--------|
| [German-RAG-PHI-CPT](https://huggingface.co/avemio/German-RAG-PHI-3.5-MINI-4B-CPT-HESSIAN-AI)   | 507.47 million |
| [German-RAG-PHI-SFT](https://huggingface.co/avemio/German-RAG-PHI-3.5-MINI-4B-SFT-HESSIAN-AI) |  2.03 billion  |  
| [German-RAG-PHI-ORPO](https://huggingface.co/avemio/German-RAG-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 MIT.
- **Contact:** [German-RAG@avemio.digital](mailto:German-RAG@avemio.digital)

### Model Sources

<!-- Provide the basic links for the model. -->

- **Training Study:** [Training Study](https://avemio.digital/wp-content/uploads/2025/01/German-RAG-TRAINING-STUDY-Advancing-German-Language-AI-with-hessian-AI.pdf)
- **Repositories:** 
    - Training: [Colab-Notebook](https://colab.research.google.com/drive/18SH_aYLCnw1K7cRGOTTZ80y98V5Kquxb?usp=sharing)
    - Evaluation code: 
        - [German-RAG-LLM-HARD-BENCHMARK](https://github.com/avemio-digital/German-RAG-LLM-HARD-BENCHMARK.git)
        - [German-RAG-LLM-EASY-BENCHMARK](https://github.com/avemio-digital/German-RAG-LLM-EASY-BENCHMARK.git)
-  **Technical blog post:**
<!-- - **Press release:** TODO -->

## Merge Details
### Merge Method

This model was merged using the SLERP merge method.

### Models Merged

The following models were included in the merge:
* [avemio/German-RAG-PHI-3.5-MINI-4B-SFT-HESSIAN-AI](https://huggingface.co/avemio/German-RAG-PHI-3.5-MINI-4B-SFT-HESSIAN-AI)
* [avemio/German-RAG-PHI-3.5-MINI-4B-ORPO-HESSIAN-AI](https://huggingface.co/avemio/German-RAG-PHI-3.5-MINI-4B-ORPO-HESSIAN-AI)

### Configuration

The following YAML configuration was used to produce this model:

```yaml
slices:
  - sources:
      - model: avemio/German-RAG-PHI-3.5-MINI-4B-SFT-HESSIAN-AI
        layer_range: [0, 32]
      - model: avemio/German-RAG-PHI-3.5-MINI-4B-ORPO-HESSIAN-AI
        layer_range: [0, 32]
merge_method: slerp
base_model: avemio/German-RAG-PHI-3.5-MINI-4B-ORPO-HESSIAN-AI
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16
```

## 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/German-RAG-PHI-3.5-MINI-4B-MERGED-HESSIAN-AI"
 
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
im_end_token_id = tokenizer.convert_tokens_to_ids('<|im_end|>')
im_start_token_id = tokenizer.convert_tokens_to_ids('<|im_start|>')
 
messages = [
    {"role": "system", "content": "Folge den Anweisungen des Benutzers. Bevor du deine finale Antwort gibst, schildere deine Überlegungen zur Lösung des Problems."},
    {"role": "user", "content": "Ferdinand steht vor der Herausforderung, eine faire Besuchsregelung für seine drei Kinder zu finden, die den Bedürfnissen jedes einzelnen Kindes gerecht wird. Jedes Kind hat unterschiedliche Vorlieben und Bedürfnisse, die in den Besuchsplan integriert werden müssen. Er muss sicherstellen, dass die Regelung sowohl den Interessen der Kinder als auch den rechtlichen Vorgaben entspricht. Ferdinand hat eine Woche Zeit, um einen Vorschlag zu erarbeiten, den er mit seinem Anwalt besprechen kann."}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=False
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
 
generated_ids = model.generate(
    **model_inputs,
    max_length=2024,
    temperature=0.01,
    do_sample=False,
    #bos_token_id=im_start_token_id,
    eos_token_id=im_end_token_id,
    pad_token_id=tokenizer.eos_token_id,
    repetition_penalty=1.1,
    num_return_sequences=1,
    top_k=40,
    top_p=0.95,
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
 
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 
```


### 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/18SH_aYLCnw1K7cRGOTTZ80y98V5Kquxb?usp=sharing).

## German-RAG-LLM-EASY-BENCHMARK EVAL

<!-- This section describes the evaluation protocols and provides the results. -->
The evaluation was performed using seven subsets, focusing on extraction recall, question answering (QA) with multiple references, and time difference reasoning. Relevant context and summarization were treated as distinct subsets, each playing a crucial role in the evaluation process. For relevant context, the model's ability to identify and extract pertinent information from the source material was assessed. In contrast, the summarization subset evaluated the model's capability to generate concise and accurate summaries based on the relevant context.

Four evaluation metrics were employed across all subsets: language quality, overall correctness, instruction following, and an overall score.

-   **Language quality:** This metric focused on the overall linguistic quality of the outputs, considering factors such as grammar, fluency, and clarity.
-   **Overall correctness:** The accuracy and correctness of the content were evaluated under this metric.
-   **Instruction following:** This metric assessed the model's ability to follow specific instructions provided for each task.
-   **Overall score:** This metric combined the results from the previous three metrics, offering a comprehensive evaluation of the model's capabilities across all subsets.


| Metric                                    | [Vanilla-Phi-3.5-Mini-4B](https://huggingface.co/ThomasComics/Phi-3-mini-128k-instruct-LLaMAfied) | [German-RAG-PHI-SFT](https://huggingface.co/avemio/German-RAG-PHI-3.5-MINI-4B-SFT-HESSIAN-AI) | [German-RAG-PHI-ORPO](https://huggingface.co/avemio/German-RAG-PHI-3.5-MINI-4B-ORPO-HESSIAN-AI) | **German-RAG-PHI-MERGED** | *GPT-3.5-TURBO* | 
|------------------------------------------|---------------------------------------------------------------------------------|--------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------|-----------------------------|----------------|
| Average Language Quality             | 75.11                                                                          | 78.88                                                                          | 78.13                                                                                          |**85.41**                             |*91.86*                |
| **OVERALL SCORES (weighted):**       |                                                                            |                                                                            |                                                                                           |                             |               |
| extraction_recall       | 18.0                                                                           | 37.5                                                                           | 32.0                                                                                          |**61.8**                             |*87.2*                |
| qa_multiple_references | 65.8                                                                           | 70.6                                                                           | 74.8                                                                                          |**84.8**                             |*77.2*                |
| qa_without_time_difference | 71.2                                                                           | 88.0                                                                           | 87.3                                                                                          |**88.0**                             |*83.1*                |
| qa_with_time_difference | 64.6                                                                           | 89.3                                                                           | 86.9                                                                                          |**89.1**                             |*83.2*                |
| relevant_context       | 72.3                                                                           | 72.8                                                                           | 69.1                                                                                          |**84.4**                             |*89.5*                |
| summarizations         | 74.6                                                                           | 83.2                                                                           | 81.1                                                                                          |**84.9**                             |*86.9*                |

## German-RAG-LLM-HARD-BENCHMARK EVAL

<img src="https://avemio.digital/wp-content/uploads/2025/01/German-RAG-PHI-Merged.png" alt="German-RAG Logo" width="700" style="margin-left:'auto' margin-right:'auto' display:'block'"/>

| Metric                  | [Vanila-PHI-4B-Instruct](https://huggingface.co/ThomasComics/Phi-3-mini-128k-instruct-LLaMAfied) | **[German-RAG-PHI-Merged](https://huggingface.co/avemio/German-RAG-PHI-3.5-MINI-4B-MERGED-HESSIAN-AI)**  | GPT-3.5-TURBO | GPT-4o | GPT-4o-mini |
|-------------------------|-----------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------|----------------|---------|-------------|
| **OVERALL SCORES (weighted):** |                                                                                 |                                                                                       |                |         |             |
| hard_reasoning_de       | 42.8                                                                                    | **41.8**                                                                                   | 37.9           | 62.9    | 58.4        |
| hard_reasoning_en       | 50.8                                                                                    | **55.9**                                                                                   | 48.3           | 61.7    | 62.9        |

### Architecture


| Parameter            | German-RAG-PHI-MERGED                                                                                  |
|-----------------------|-----------------------------------------------------------------------------------------------|
| **d_model**          | 3072                                                                                          |
| **num heads**        | 32                                                                                            |
| **num layers**       | 32                                                                                            |
| **MLP ratio**        | 2.66                                                                                          |
| **LayerNorm type**   | RMSNorm                                                                                       |
| **pos embeddings**   | RoPE                                                                                          |
| **attention variant**| Standard Multi-Head Self Attention with sliding-window of 2047                                |
| **biases**           | none                                                                                          |
| **block type**       | sequential                                                                                    |
| **activation**       | SiLU                                                                                          |
| **sequence length**  | 131072                                                                                        |
| **weight tying**     | bfloat16  



## 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 German-RAG-PHI-MERGED or any LLM will often not be true, so they should be checked.


## The German-RAG AI Team
[Marcel Rosiak](https://de.linkedin.com/in/marcel-rosiak)
[Soumya Paul](https://de.linkedin.com/in/soumya-paul-1636a68a)
[Siavash Mollaebrahim](https://de.linkedin.com/in/siavash-mollaebrahim-4084b5153?trk=people-guest_people_search-card)
[Zain ul Haq](https://de.linkedin.com/in/zain-ul-haq-31ba35196)