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library_name: transformers
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tags:
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---
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# Model Card for Model ID
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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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[
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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[More Information Needed]
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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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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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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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- **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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### Model Architecture and Objective
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[More Information Needed]
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[More Information Needed]
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##
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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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- llama3
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- english
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license: llama3
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datasets:
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- seedboxai/multitask_german_examples_32k
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language:
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- de
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- en
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pipeline_tag: text-generation
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# Llama-3-KafkaLM-8B-v0.1
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**KafkaLM 8b** is a Llama3 8b model which was finetuned on an ensemble of popular high-quality open-source instruction sets (translated from English to German).
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Llama 3 KafkaLM 8b 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, and 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 is 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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### 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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### 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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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "seedboxai/Llama-3-Kafka-8B-v0.1"
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)
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tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
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pipeline = 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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device="cuda",
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)
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messages = [
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{"role": "system", "content": "Du bist ein hilfreicher KI-Assistent."},
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{"role": "user", "content": "Wer ist eigentlich dieser Kafka?"},
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]
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prompt = pipeline.tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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terminators = [
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pipeline.tokenizer.eos_token_id,
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pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
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]
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outputs = pipeline(
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prompt,
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max_new_tokens=max_new_tokens,
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num_beams=5,
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num_return_sequences=1,
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early_stopping=True,
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eos_token_id=terminators,
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do_sample=True,
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temperature=temperature,
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top_p=0.9,
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)
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print(outputs[0]["generated_text"][len(prompt):])
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```
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```
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<|start_header_id|>system<|end_header_id|>Du bist ein hilfreicher KI-Assistent.<|eot_id|>
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<|start_header_id|>user<|end_header_id|>Wer ist eigentlich dieser Kafka?<|eot_id|>
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<|start_header_id|>assistant<|end_header_id|>Franz Kafka (1883-1924) war ein tschechisch-österreichischer Schriftsteller,
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der als einer der bedeutendsten Autoren des 20. Jahrhunderts gilt. Seine Werke sind geprägt von existenzieller Angst, Isolation, Verwirrung und Absurdität.
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Kafka wurde in Prag, Österreich-Ungarn (heute Tschechische Republik), geboren und wuchs in einer jüdischen Familie auf.
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Er studierte Rechtswissenschaften und arbeitete als Versicherungsangestellter, bevor er sich dem Schreiben zuwandte.
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Kafkas literarisches Werk umfasst Romane, Erzählungen, Briefe und Tagebücher. Zu seinen bekanntesten Werken gehören "Der Prozess" (1925),
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"Das Schloss" (1926) und "Die Verwandlung" (1915). Seine Geschichten sind geprägt von surrealen, traumähnlichen Szenarien,
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in denen die Charaktere oft in Situationen geraten, die sie nicht verstehen oder kontrollieren können.
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Kafkas Werk hat einen tiefgreifenden Einfluss auf die Literatur und Kultur des 20. Jahrhunderts gehabt.
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Seine Werke wurden in viele Sprachen übersetzt und inspirierten Autoren wie Albert Camus, Jean-Paul Sartre, Samuel Beckett und Thomas Mann.
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Kafka starb 1924 im Alter von 40 Jahren an Tuberkulose. Trotz seines relativ kurzen Lebens hat er einen bleibenden Eindruck auf die Literatur und Kultur hinterlassen.
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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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