content.raw: Check this out: I trained an AI on huggingface posts! all of these are AI generated:
content.type: text
content.value: Check this out: I trained an AI on huggingface posts! all of these are AI generated:
content.type: new_line
content.raw: ----------
content.type: text
content.value: ----------
content.type: new_line
content.raw: Hello!
content.type: text
content.value: Hello!
content.type: new_line
content.type: new_line
content.raw: I'm excited to share that my colleague 
content.type: text
content.value: I'm excited to share that my colleague 
content.raw: @felipeebert
content.type: mention
content.user: felipeebert
content.raw:  and I have released the largest Spanish LLM benchmark to date. 
content.type: text
content.value:  and I have released the largest Spanish LLM benchmark to date. 
content.type: new_line
content.type: new_line
content.raw: We've developed the Spanish LLM Evaluation Benchmark (SLAB), a set of benchmarks designed to evaluate the ability of language models to understand, generate and translate in Spanish.   
content.type: text
content.value: We've developed the Spanish LLM Evaluation Benchmark (SLAB), a set of benchmarks designed to evaluate the ability of language models to understand, generate and translate in Spanish.   
content.type: new_line
content.type: new_line
content.raw: SLAB includes five different benchmarks:
content.type: text
content.value: SLAB includes five different benchmarks:
content.type: new_line
content.raw: - Sentiment Analysis: evaluate models' ability to detect and describe sentiment in natural language
content.type: text
content.value: - Sentiment Analysis: evaluate models' ability to detect and describe sentiment in natural language
content.type: new_line
content.raw: - Fact Checking: evaluate models' ability to detect and refute factual errors in text
content.type: text
content.value: - Fact Checking: evaluate models' ability to detect and refute factual errors in text
content.type: new_line
content.raw: - Question Answering: evaluate models' ability to answer questions in Spanish
content.type: text
content.value: - Question Answering: evaluate models' ability to answer questions in Spanish
content.type: new_line
content.raw: - Open-ended Questions: evaluate models' ability to generate coherent responses in Spanish
content.type: text
content.value: - Open-ended Questions: evaluate models' ability to generate coherent responses in Spanish
content.type: new_line
content.raw: - Translation: evaluate models' ability to translate in Spanish
content.type: text
content.value: - Translation: evaluate models' ability to translate in Spanish
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content.type: new_line
content.raw: SLAB is aligned with the latest Spanish LLM industry developments and includes the most recent models available on the market. We aim to keep our benchmarks up-to-date and relevant to the Spanish language ecosystem.
content.type: text
content.value: SLAB is aligned with the latest Spanish LLM industry developments and includes the most recent models available on the market. We aim to keep our benchmarks up-to-date and relevant to the Spanish language ecosystem.
content.type: new_line
content.type: new_line
content.raw: SLAB is available at: 
content.type: text
content.value: SLAB is available at: 
content.href: https://huggingface.co/datasets/argilla/SLAB
content.raw: https://huggingface.co/datasets/argilla/SLAB
content.type: link
content.raw: . 
content.type: text
content.value: . 
content.type: new_line
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content.raw: If you would like to collaborate on building additional Spanish LLM benchmarks, let's discuss in the comments.
content.type: text
content.value: If you would like to collaborate on building additional Spanish LLM benchmarks, let's discuss in the comments.
content.type: new_line
content.type: new_line
content.raw: 🔗 SLAB Blog Post: 
content.type: text
content.value: 🔗 SLAB Blog Post: 
content.href: https://argilla.com/blog/slab
content.raw: https://argilla.com/blog/slab
content.type: link
content.type: new_line
content.raw: ----------
content.type: text
content.value: ----------
content.type: new_line
content.raw: Hello everyone,
content.type: text
content.value: Hello everyone,
content.type: new_line
content.type: new_line
content.raw: I'm thrilled to announce the release of 
content.type: text
content.value: I'm thrilled to announce the release of 
content.type: new_line
content.type: new_line
content.href: https://huggingface.co/01-AI/01AI-GPT-4o
content.raw: https://huggingface.co/01-AI/01AI-GPT-4o
content.type: link
content.raw:  -
content.type: text
content.value:  -
content.type: new_line
content.type: new_line
content.raw: A new family of models that brings the power of transformer AI to the masses.
content.type: text
content.value: A new family of models that brings the power of transformer AI to the masses.
content.type: new_line
content.type: new_line
content.raw: This model is designed to be accessible and easy to use, while still offering high-quality results.
content.type: text
content.value: This model is designed to be accessible and easy to use, while still offering high-quality results.
content.type: new_line
content.type: new_line
content.raw: Key features:
content.type: text
content.value: Key features:
content.type: new_line
content.raw: - Small model size: only 23M parameters
content.type: text
content.value: - Small model size: only 23M parameters
content.type: new_line
content.raw: - Supports text generation, image generation, and text-to-image tasks
content.type: text
content.value: - Supports text generation, image generation, and text-to-image tasks
content.type: new_line
content.raw: - Data-efficient training with a lightweight tokenizer
content.type: text
content.value: - Data-efficient training with a lightweight tokenizer
content.type: new_line
content.raw: - Optimized for efficient on-device usage
content.type: text
content.value: - Optimized for efficient on-device usage
content.type: new_line
content.raw: - Uses the powerful transformer architecture to deliver high-quality results
content.type: text
content.value: - Uses the powerful transformer architecture to deliver high-quality results
content.type: new_line
content.type: new_line
content.raw: Excited to see what you all think!
content.type: text
content.value: Excited to see what you all think!
content.type: new_line
content.type: new_line
content.href: https://huggingface.co/01-AI/01AI-GPT-4o
content.raw: https://huggingface.co/01-AI/01AI-GPT-4o
content.type: link
rawContent: Check this out: I trained an AI on huggingface posts! all of these are AI generated:
  ----------
  Hello!
  
  I'm excited to share that my colleague @felipeebert and I have released the largest Spanish LLM benchmark to date. 
  
  We've developed the Spanish LLM Evaluation Benchmark (SLAB), a set of benchmarks designed to evaluate the ability of language models to understand, generate and translate in Spanish.   
  
  SLAB includes five different benchmarks:
  - Sentiment Analysis: evaluate models' ability to detect and describe sentiment in natural language
  - Fact Checking: evaluate models' ability to detect and refute factual errors in text
  - Question Answering: evaluate models' ability to answer questions in Spanish
  - Open-ended Questions: evaluate models' ability to generate coherent responses in Spanish
  - Translation: evaluate models' ability to translate in Spanish
  
  SLAB is aligned with the latest Spanish LLM industry developments and includes the most recent models available on the market. We aim to keep our benchmarks up-to-date and relevant to the Spanish language ecosystem.
  
  SLAB is available at: https://huggingface.co/datasets/argilla/SLAB. 
  
  If you would like to collaborate on building additional Spanish LLM benchmarks, let's discuss in the comments.
  
  🔗 SLAB Blog Post: https://argilla.com/blog/slab
  ----------
  Hello everyone,
  
  I'm thrilled to announce the release of 
  
  https://huggingface.co/01-AI/01AI-GPT-4o -
  
  A new family of models that brings the power of transformer AI to the masses.
  
  This model is designed to be accessible and easy to use, while still offering high-quality results.
  
  Key features:
  - Small model size: only 23M parameters
  - Supports text generation, image generation, and text-to-image tasks
  - Data-efficient training with a lightweight tokenizer
  - Optimized for efficient on-device usage
  - Uses the powerful transformer architecture to deliver high-quality results
  
  Excited to see what you all think!
  
  https://huggingface.co/01-AI/01AI-GPT-4o
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reactions.users: John6666 takeraparterer Clausss xi0v raincandy-u Svngoku
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