bielik-11b-v2.3-instruct

Maintainer: aleksanderobuchowski - Last updated 12/8/2024

Model overview

The bielik-11b-v2.3-instruct model is a generative text model with 11 billion parameters developed by SpeakLeash and Cyfronet. It is an instruction-tuned model that has been fine-tuned on a collection of Polish text data and instruction datasets. The model builds upon previous versions of the Bielik model, including Bielik-11B-v2.0-Instruct, Bielik-11B-v2.1-Instruct, and Bielik-11B-v2.2-Instruct, incorporating improvements and optimizations from each iteration.

Model inputs and outputs

The bielik-11b-v2.3-instruct model takes in text prompts as input and generates continued text as output. The model has been designed to provide concise and precise responses in the Polish language.

Inputs

Input: The text prompt to send to the model.
Max Length: The maximum number of tokens to generate, with a word generally being 2-3 tokens.
Repetition Penalty: A penalty applied to repeated words in the generated text, with a value of 1 meaning no penalty and values greater than 1 discouraging repetition.
Temperature: Adjusts the randomness of the outputs, with a higher temperature resulting in more random and diverse text.
Top P: Determines the percentage of the most likely tokens to sample from during decoding, with a lower value ignoring less likely tokens.

Outputs

Output: The generated text completion.

Capabilities

The bielik-11b-v2.3-instruct model has been designed to excel at natural language processing tasks in the Polish language. It has demonstrated strong performance on benchmarks such as the Open PL LLM Leaderboard, the Open LLM Leaderboard, the Polish MT-Bench, and the Polish EQ-Bench. The model has also shown promising results on the MixEval benchmark, which evaluates language models on a diverse set of English tasks.

What can I use it for?

The bielik-11b-v2.3-instruct model can be utilized for a variety of Polish language processing tasks, such as text generation, question answering, and language modeling. The model's strong performance on benchmarks suggests it could be a valuable resource for natural language processing projects and applications targeting the Polish market.

Things to try

One interesting aspect of the bielik-11b-v2.3-instruct model is its efficient performance relative to its size. Despite having fewer parameters than some larger models, it has demonstrated competitive or even superior results on various evaluation tasks. This efficiency could make the model an attractive option for projects with limited computational resources or where performance needs to be balanced against model size.

This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

Follow @aimodelsfyi on 𝕏 →

Related Models

🔎

Bielik-11B-v2.2-Instruct

speakleash

Bielik-11B-v2.2-Instruct is a generative text model featuring 11 billion parameters. It is an instruct fine-tuned version of the Bielik-11B-v2 model. The model was developed and trained on Polish text corpora by the SpeakLeash team, leveraging the computing infrastructure and support of the High Performance Computing (HPC) center: ACK Cyfronet AGH. This collaboration enabled the use of cutting-edge technology and computational resources essential for large-scale machine learning processes. As a result, the model exhibits an exceptional ability to understand and process the Polish language, providing accurate responses and performing a variety of linguistic tasks with high precision. The Bielik-7B-Instruct-v0.1 is another instruct fine-tuned model from the SpeakLeash team, featuring 7 billion parameters. It was developed using a similar approach, leveraging Polish computing infrastructure and datasets to create a highly capable Polish language model. Model Inputs and Outputs Inputs Textual prompts in Polish language Outputs Textual completions in Polish language, continuing the input prompt Capabilities Bielik-11B-v2.2-Instruct demonstrates exceptional performance in understanding and generating Polish text. It can be used for a variety of natural language processing tasks, such as: Question Answering**: The model can provide accurate and contextual answers to questions in Polish. Text Generation**: The model can generate coherent and fluent Polish text, ranging from short responses to longer-form content. Summarization**: The model can summarize Polish text, capturing the key points and ideas. Translation**: While primarily focused on Polish, the model can also perform translation between Polish and other languages. What Can I Use It For? The Bielik-11B-v2.2-Instruct model is well-suited for applications that require a high degree of accuracy and reliability in processing the Polish language. Some potential use cases include: Content Creation**: The model can be used to generate Polish articles, reports, or creative writing, saving time and effort for content creators. Chatbots and Virtual Assistants**: The model can power Polish-language chatbots and virtual assistants, providing natural and engaging conversations. Language Learning**: The model can be integrated into educational tools and apps to assist with Polish language learning and practice. Document Processing**: The model can be used to analyze and extract insights from Polish business documents, legal contracts, and other types of text-based content. Things to Try One interesting aspect of the Bielik-11B-v2.2-Instruct model is its ability to follow instructions and generate text based on specific prompts. You can experiment with providing the model with various types of instructions, such as: Creative Writing**: Give the model a prompt to write a short story or poem in Polish, and see how it responds. Task Completion**: Provide the model with a task or set of instructions in Polish, and observe how it attempts to complete the task. Q&A**: Ask the model a series of questions in Polish and see how it responds, testing its understanding and reasoning capabilities. By exploring the model's response to different types of prompts and instructions, you can gain a deeper understanding of its capabilities and potential applications.

Updated 10/16/2024

Text-to-Text

🔄

Bielik-7B-Instruct-v0.1

speakleash

The Bielik-7B-Instruct-v0.1 is an instruct fine-tuned version of the Bielik-7B-v0.1 model. It was developed and trained on Polish text corpora by the SpeakLeash team, leveraging the High Performance Computing (HPC) center ACK Cyfronet AGH. This collaboration enabled the use of cutting-edge technology and computational resources essential for large-scale machine learning processes. As a result, the model exhibits an exceptional ability to understand and process the Polish language, providing accurate responses and performing a variety of linguistic tasks with high precision. The Bielik-7B-Instruct-v0.1 has been trained using an original open source framework called ALLaMo, implemented by Krzysztof Ociepa. Several improvements were introduced to the training process, including weighted tokens level loss, adaptive learning rate, and masked user instructions. The model has been evaluated on the Open PL LLM Leaderboard, showcasing its strong performance in tasks like sentiment analysis, categorization, and text classification. The Bielik-7B-Instruct-v0.1 model surpasses the Bielik-7B-v0.1 in several metrics, demonstrating the benefits of instruct fine-tuning. Model inputs and outputs Inputs Natural language text**: The Bielik-7B-Instruct-v0.1 model can process a wide range of Polish language inputs, from short prompts to longer passages of text. Outputs Natural language text**: The model generates coherent and contextually relevant Polish language outputs, such as responses, translations, or generated text, based on the provided inputs. Capabilities The Bielik-7B-Instruct-v0.1 model is capable of performing a variety of natural language processing tasks in the Polish language, including: Text generation**: The model can generate fluent and coherent Polish language text, making it useful for tasks like content creation, story generation, and question answering. Text understanding**: The model can accurately comprehend and interpret Polish language inputs, enabling applications like sentiment analysis, text classification, and question answering. Translation**: The model can translate between Polish and other languages, facilitating cross-lingual communication and content sharing. What can I use it for? The Bielik-7B-Instruct-v0.1 model can be leveraged for a wide range of applications in the Polish language market, such as: Content creation**: Generate high-quality Polish language content for websites, blogs, social media, and other digital platforms. Chatbots and virtual assistants**: Develop Polish-language chatbots and virtual assistants that can engage in natural conversations and provide helpful information to users. Language learning and education**: Create interactive language learning tools and educational materials to help Polish speakers improve their language skills. Multilingual communication**: Facilitate seamless communication and collaboration between Polish speakers and individuals from other language backgrounds. Things to try One interesting aspect of the Bielik-7B-Instruct-v0.1 model is its ability to maintain language consistency during multi-turn dialogues. By following the provided instruction format, users can engage the model in back-and-forth conversations and observe how it maintains the appropriate Polish language usage throughout the exchange. Another intriguing possibility is to explore the model's performance on specialized Polish language tasks, such as legal document processing, technical writing, or domain-specific question answering. By tailoring the prompts and fine-tuning the model further, users can unlock the full potential of the Bielik-7B-Instruct-v0.1 in niche applications.

Updated 9/6/2024

Text-to-Text

👁️

Bielik-7B-v0.1

speakleash

The Bielik-7B-v0.1 is a Polish language model with 7 billion parameters, developed through a collaboration between the open-source project SpeakLeash and the High Performance Computing (HPC) center ACK Cyfronet AGH. The model was trained on over 36 billion tokens of Polish text, building upon the foundation of the previous Mistral-7B-v0.1 model. This effort leveraged the computational power of the Helios supercomputer, enabling the model to achieve exceptional performance in understanding and processing the Polish language. Model inputs and outputs The Bielik-7B-v0.1 is a causal decoder-only model, which means it takes in text and generates new text based on the input. The model can handle a variety of Polish language tasks, such as text generation, summarization, and language understanding. Inputs Polish text Outputs Generated Polish text Summarized Polish text Responses to Polish language tasks Capabilities The Bielik-7B-v0.1 model exhibits exceptional capabilities in understanding and generating Polish text. It can perform a wide range of linguistic tasks, such as answering questions, generating coherent and contextual responses, and summarizing Polish documents. The model's high-quality outputs are a testament to the dedication of the SpeakLeash team and the computational resources provided by the ACK Cyfronet AGH HPC center. What can I use it for? The Bielik-7B-v0.1 model can be employed in a variety of applications that require Polish language processing, such as chatbots, content generation, and language understanding systems. Developers and researchers can leverage the model's capabilities to build innovative solutions that cater to the needs of Polish-speaking audiences. The model's open-source nature and commercial use allowance make it accessible to a wide range of users. Things to try Developers and researchers can experiment with the Bielik-7B-v0.1 model to explore its capabilities in depth. For instance, you can fine-tune the model on domain-specific Polish text to enhance its performance on specialized tasks. Additionally, you can compare the model's outputs with other Polish language models to gain insights into its strengths and potential areas for improvement.

Updated 7/10/2024

Text-to-Text

mistral-7b-instruct-v0.2

tomasmcm

The mistral-7b-instruct-v0.2 is an improved instruct fine-tuned version of the Mistral-7B-Instruct-v0.1 Large Language Model (LLM) from Mistral AI. It builds upon the original Mistral-7B-v0.1 model, which uses a transformer architecture with grouped-query attention, sliding-window attention, and a byte-fallback BPE tokenizer. Model inputs and outputs The mistral-7b-instruct-v0.2 model takes a text prompt as input, with the prompt enclosed in [INST] and [/INST] tokens. The first instruction in the prompt should begin with a begin-of-sentence token, while subsequent instructions do not need it. The model will generate output text token-by-token, stopping when an end-of-sentence token is produced. Inputs prompt**: The text prompt to be processed by the model, enclosed in [INST] and [/INST] tokens. max_tokens**: The maximum number of tokens to generate in the output. temperature, **top_k, top_p, presence_penalty, frequency_penalty: Hyperparameters that control the randomness and diversity of the generated output. stop**: A list of strings that will stop the generation if encountered. Outputs output**: The generated text, which can be up to max_tokens tokens long. Capabilities The mistral-7b-instruct-v0.2 model is capable of generating coherent and relevant text based on the provided prompt. It can engage in open-ended conversations, answer questions, and perform various language tasks such as summarization, translation, and text generation. The model's instruction-following capabilities allow it to adapt to specific prompts and tailor its output accordingly. What can I use it for? The mistral-7b-instruct-v0.2 model can be used in a variety of applications, such as building chatbots, virtual assistants, and content generation tools. Its ability to follow instructions and generate relevant text makes it a useful tool for tasks like customer service, content creation, and research assistance. However, the model does not have any moderation mechanisms, so it should be used with caution in environments requiring carefully controlled outputs. Things to try One interesting thing to try with the mistral-7b-instruct-v0.2 model is to experiment with different prompt formats and styles. The model's instruction-following capabilities allow it to adapt to a wide range of prompts, so you can try framing your requests in different ways to see how the model responds. Additionally, you can explore the model's ability to engage in multi-turn conversations by providing a series of prompts and instructions.

Updated 12/8/2024

Text-to-Text