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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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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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-
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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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-
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- ### Out-of-Scope Use
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-
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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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- <!-- 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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- #### Preprocessing [optional]
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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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- ### Results
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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 Architecture and Objective
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- [More Information Needed]
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- ### Compute Infrastructure
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- #### Hardware
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- [More Information Needed]
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- #### Software
 
 
 
 
 
 
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- [More Information Needed]
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
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- **BibTeX:**
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- [More Information Needed]
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- **APA:**
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- [More Information Needed]
 
 
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- ## Glossary [optional]
 
 
 
 
 
 
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
 
 
 
 
 
 
 
 
 
 
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- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
 
 
 
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- ## Model Card Authors [optional]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- [More Information Needed]
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- ## Model Card Contact
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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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+ - llama2
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+ - deutsch
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+ - german
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+ - seedbox
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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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+ - en
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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/oh7yRzqtRlDtdu8sJoAdV.jpeg)
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+ # KafkaLM-7B-German-V0.1
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+ **KafkaLM 70b** is a Mistral 7b model - further pre-trained on a large german dataset from Björn Plüster and LAION. [leo-mistral-hessianai-7b](https://huggingface.co/LeoLM/leo-mistral-hessianai-7b) - which was finetuned on an ensemble of popular high-quality open-source instruction sets (translated from English to German).
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+ KafkaLM 7b 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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+ ### 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-7B-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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+
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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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+ def evaluate(
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+ input,
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+ temperature=0.7,
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+ top_p=0.95,
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+ top_k=50,
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+ num_beams=3,
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+ max_new_tokens=512,
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+ #max_length=8192,
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+ **kwargs,
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+ ):
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+ prompt = generate_prompt(input)
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+ #print(prompt)
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+
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+ inputs = tokenizer(prompt, return_tensors="pt")
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+ input_ids = inputs["input_ids"].to(device)
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+ attention_mask=inputs["attention_mask"].to(device)
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+ generation_config = GenerationConfig(
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+ temperature=temperature,
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+ top_p=top_p,
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+ top_k=top_k,
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+ num_beams=num_beams,
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+ no_repeat_ngram_size=3,
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+ do_sample=True,
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+ **kwargs,
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+ )
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+ with torch.no_grad():
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+ generation_output = model.generate(
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+ early_stopping=False,
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+ #eos_token_id=tokenizer.eos_token_id,
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+ #pad_token_id=tokenizer.pad_token_id,
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+ input_ids=input_ids,
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+ attention_mask=attention_mask,
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+ generation_config=generation_config,
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+ return_dict_in_generate=True,
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+ output_scores=True,
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+ max_new_tokens=max_new_tokens,
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+ #max_length= max_length
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+ )
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+ s = generation_output.sequences[0]
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+ output = tokenizer.decode(s)
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+ return output #.split("<|assistant|>")[1].strip()
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+ print(evaluate("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.