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- library_name: transformers
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- tags: []
 
 
 
 
 
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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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  ### 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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- <!-- 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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  #### 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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- ## 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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- **APA:**
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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 [optional]
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- ## Model Card Authors [optional]
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  ## Model Card Contact
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- [More Information Needed]
 
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+ base_model: TurkuNLP/gpt3-finnish-3B
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+ license: apache-2.0
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+ datasets:
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+ - TurkuNLP/squad_v2_fi
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+ language:
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+ - fi
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+ pipeline_tag: text-generation
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  ---
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+ # Model Card for Model Futurice/gpt3-finnish-3B-instruct
 
 
 
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+ The model gpt3-finnish-3B-instruct is an instruction fine-tuned model intended for RAG type Q&A in Finnish.
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  ## Model Details
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  ### Model Description
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+ The gpt3-finnish-3B-instruct model is based on TurkuNLP Finnish GPT-3-models. They are a model family of pretrained monolingual GPT-style language models, based on BLOOM-architecture.
 
 
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+ The model was fine-tuned using a sample of dataset TurkuNLP/squad_v2_fi, that was DeepL translated from SQuAD2.0.
 
 
 
 
 
 
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+ - **Developed by:** Martti Sutinen
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+ - **Model type:** Bloom
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+ - **Language(s) (NLP):** Finnish
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+ - **License:** Apache-2.0
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+ - **Finetuned from model:** TurkuNLP/gpt3-finnish-large
 
 
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  ## Uses
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+ Intended for RAG type Q&A in Finnish.
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  ### Direct Use
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+ Intended for text generation and RAG type Q&A in Finnish. Supply a context and ask a question about it.
 
 
 
 
 
 
 
 
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  ### Out-of-Scope Use
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+ Please do not misuse the model. Not recommended for other use cases.
 
 
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  ## Bias, Risks, and Limitations
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+ A key limitation is simple and limited selection of fine-tuning data. Please do not expect high quality answers.
 
 
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  ### Recommendations
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+ Recommeded to continue fine-tuning with more data or newer architecture.
 
 
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  ## How to Get Started with the Model
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+ - Recommended system message: "Olet avustaja. Seuraavaksi saat kysymyksen tai tehtävän. Kirjoita vastaus parhaasi mukaan siten että se täyttää kysymyksen tai tehtävän vaatimukset."
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+ - Recommended format for question about context: Tausta: "{context} \n\nKäytä vain taustaa ja vastaa kysymykseen tai tehtävään: {question}"
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+ - Prompt format: tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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+ Where messages with typical format:
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+ messages = [
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+ {"role": "system", "content": system_message},
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+ {"role": "user", "content": prompt_with_context}
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+ ].
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+ Here is what the input could look like:
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+ \<s><|im_start|>system
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+ Olet avustaja. Seuraavaksi saat kysymyksen tai tehtävän. Kirjoita vastaus parhaasi mukaan siten että se täyttää kysymyksen tai tehtävän vaatimukset.<|im_end|>
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+ <|im_start|>user
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+ Tausta:
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+ Dokumentti luotiin tammikuussa. Sen kirjoittajaa ei tunneta.
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+ Käytä vain taustaa ja vastaa kysymykseen tai tehtävään: Milloin dokumentti kirjoitettiin?<|im_end|>
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+ <|im_start|>assistant
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+ Use pipeline with task text-generation and the recommended format.
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+ ## Training Details
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+ ### Training Data
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+ Trained with 20000 random samples from test data in: [TurkuNLP/squad_v2_fi](https://huggingface.co/datasets/TurkuNLP/squad_v2_fi).
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+ ### Training Procedure
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+ Training was done for 4-bit base model with supervised fine-tuning and Lora.
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  #### Training Hyperparameters
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+ - **Training regime:** 4-bit, batch size 2, max steps 20000, data collator for completion only
 
 
 
 
 
 
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  ## Evaluation
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+ Evaluation has not been done properly yet.
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  ### Testing Data, Factors & Metrics
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  #### Testing Data
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+ Evaluated with 1000 random samples from test data in: [TurkuNLP/squad_v2_fi](https://huggingface.co/datasets/TurkuNLP/squad_v2_fi).
 
 
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  #### Factors
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+ Same factors as in SQuAD2.0.
 
 
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  #### Metrics
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+ Loss.
 
 
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  ### Results
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+ No results to be shared yet.
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  #### Summary
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  ## Environmental Impact
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+ Environmental impact not yet evaluated.
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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:** Mostly trained on A100
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+ - **Hours used:** 5-10 hours
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+ - **Cloud Provider:** GCP
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+ - **Compute Region:** Unknown
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+ - **Carbon Emitted:** Not evaluated
 
 
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  ### Model Architecture and Objective
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+ Bloom.
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  ### Compute Infrastructure
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+ Colab.
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  #### Hardware
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+ 1 x A100.
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  #### Software
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+ Typical software used.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## Model Card Contact
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+ Martti Sutinen