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README.md
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license: cc-by-4.0
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---
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# Model Card for Model ID
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### Model Description
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## Uses
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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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<!-- 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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[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 Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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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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<!-- 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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## Training procedure
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### Framework versions
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- PEFT 0.6.2
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base_model: LeoLM/leo-hessianai-7b
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license: cc-by-4.0
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datasets:
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- caretech-owl/wikiquote-de-quotes
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language:
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library_name: adapter-transformers
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pipeline_tag: text-generation
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# Model Card for Model ID
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This model is trained to generate german quotes for a given author.
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The full model can be tested at [spaces/caretech-owl/quote-generator-de](https://huggingface.co/spaces/caretech-owl/quote-generator-de),
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here we provide the LORA adapter files for loading on top of the base model [LeoLM/leo-hessianai-7b](https://huggingface.co/LeoLM/leo-hessianai-7b).
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### Model Description
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This fine-tuned model has been trained on the [caretech-owl/wikiquote-de-quotes](https://huggingface.co/datasets/caretech-owl/wikiquote-de-quotes) dataset.
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The model was trained on a prompt like this
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```python
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prompt_format = "<|im_start|>system\
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Dies ist eine Unterhaltung zwischen einem\
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intelligenten, hilfsbereitem KI-Assistenten und einem Nutzer.
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Der Assistent gibt Antworten in Form von Zitaten.<|im_end|>\n\
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<|im_start|>user\
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Zitiere {author}<|im_end|>\n<\
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|im_start|>assistant\n{quote}<|im_end|>\n"
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```
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Where author is itended to be provided by the user, the quote is of format ```quote + " - " + author```.
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While the model is not able to provide "real" quotes, using authors that are part of the training set and
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a low temperature for generation results in somewhat realistic quotes that at least sound familiar.
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- **Developed by:** [CareTech OWL](https://www.caretech-owl.de/)
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- **Model type:** LLAMA2 LORA adapter
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- **Language(s) (NLP):** German
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- **License:** [CC-BY 4.0](https://creativecommons.org/licenses/by/4.0/)
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- **Finetuned from model:** [LeoLM/leo-hessianai-7b](https://huggingface.co/LeoLM/leo-hessianai-7b)
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## Uses
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```python
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from transformers import (
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AutoModelForCausalLM,
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AutoTokenizer,
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pipeline
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)
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base_model = AutoModelForCausalLM.from_pretrained('LeoLM/leo-hessianai-7b')
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tokenizer = AutoTokenizer.from_pretrained(base_model_name,
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trust_remote_code=False)
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tokenizer.pad_token = tokenizer.eos_token
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base_model.load_adapter('caretech-owl/leo-hessianai-7B-ggpq-german-quotes-lora', adapter_name='leo-hessianai-7B-ggpq-german-quotes-lora')
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base_model.enable_adapters()
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text_gen = pipeline(task="text-generation", model=base_model,
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max_length=200, tokenizer=tokenizer)
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system_prompt = """Dies ist eine Unterhaltung zwischen \
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einem intelligenten, hilfsbereitem \
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KI-Assistenten und einem Nutzer.
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Der Assistent gibt Antworten in Form von Zitaten."""
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prompt_format = "<|im_start|>system\n{system_prompt}\
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<|im_end|>\n<|im_start|>user\nZitiere {prompt}\
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<|im_end|>\n<|im_start|>assistant\n"
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def get_quote(author:str, max_length:int=200):
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query = prompt_format.format(system_prompt=system_prompt, prompt= author)
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output = text_gen(query, do_sample=True, top_p=0.95, max_length=max_length,
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return_full_text=False, pad_token_id=tokenizer.pad_token_id)
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print(output[0]['generated_text'])
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get_quote("Heinrich Heine")
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```
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## Training procedure
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### Framework versions
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- PEFT 0.6.2
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