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library_name: transformers
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tags:
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---
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<!-- Provide a quick summary of what the model is/does. -->
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- 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. -->
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[More Information Needed]
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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## Model Card Authors [optional]
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library_name: transformers
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tags:
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- deutsch
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- german
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- seedbox
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- mistral
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- mixtral
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license: apache-2.0
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datasets:
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- seedboxai/multitask_german_examples_32k
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- seedboxai/ultra_feedback_german_modified_v1
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language:
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- de
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pipeline_tag: text-generation
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---
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![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/645ded34a45b4182d7f5c385/9QywLGTbRrHYSq-m6fQmJ.jpeg)
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# KafkaLM-8x7b-German-V0.1
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**KafkaLM 8x7b** is a MoE model based on [Mistral AI´s Mixtral 8x7b](https://mistral.ai/news/mixtral-of-experts/) which was finetuned on an ensemble of popular high-quality open-source instruction sets (translated from English to German).
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KafkaLM 8x7b is a [Seedbox](https://huggingface.co/seedboxai) project trained by [Dennis Dickmann](https://huggingface.co/doubledsbv).
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**Why Kafka?**
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The models are proficient, yet creative, have some tendencies to linguistically push boundaries 😊
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## Model Details
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The purpose of releasing the **KafkaLM series** is to contribute to the German AI community with a set of fine-tuned LLMs that are easy to use in everyday applications across a variety of tasks.
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The main goal was to provide LLMs proficient in German, especially to be used in German-speaking business contexts where English alone is not sufficient.
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### DPO
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The model has been aligned with a german and modified version of the ultra feedback dataset from huggingface.
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### Dataset
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I used a 8k filtered version of the following [seedboxai/multitask_german_examples_32k](https://huggingface.co/datasets/seedboxai/multitask_german_examples_32k)
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### Prompt Format
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This model follows the subsequent prompt format:
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```
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<|system|>
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Du bist ein freundlicher und hilfsbereiter KI-Assistent. Du beantwortest Fragen faktenorientiert und präzise, ohne dabei relevante Fakten auszulassen.</s>
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<|user|>
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Welche Möglichkeiten der energetischen Sanierung habe ich neben Solar und Energiespeicher?</s>
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<|assistant|>
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```
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### Inference
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Getting started with the model is straightforward
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```python
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import transformers
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model_id = "seedboxai/KafkaLM-8x7B-German-V0.1"
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model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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tokenizer.padding_side = "right"
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tokenizer.pad_token = tokenizer.unk_token
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tokenizer.add_eos_token = False
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def generate_prompt(input):
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prompt = ''
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sys_prompt = "Du bist ein freundlicher und hilfsbereiter KI-Assistent. Du beantwortest Fragen faktenorientiert und präzise, ohne dabei relevante Fakten auszulassen."
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prompt += f"<|system|>\n{sys_prompt.strip()}</s>\n"
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prompt += f"<|user|>\n{input.strip()}</s>\n"
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prompt += f"<|assistant|>\n"
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return prompt.strip()
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generate_text = transformers.pipeline(
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model=model, tokenizer=tokenizer,
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return_full_text=True,
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task='text-generation',
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temperature=0.5,
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max_new_tokens=512,
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top_p=0.95,
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top_k=40,
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do_sample=True,
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)
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print(generate_text(generate_prompt("Wer ist eigentlich dieser Kafka?"))
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```
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## Disclaimer
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The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model.
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This model should only be used for research purposes. The original Llama2 license and all restrictions of datasets used to train this model apply.
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