lucataco/ollama-qwen2.5-72b 🔢📝 → 📝

▶️ 27.1K runs 📅 Sep 2024 ⚙️ Cog 0.9.20 🔗 GitHub 📄 Paper ⚖️ License
code-generation text-generation text-translation

About

Ollama Qwen2.5 72b

Example Output

Prompt:

"Give me a short introduction to large language model"

Output

Certainly! A Large Language Model (LLM) is an advanced type of artificial intelligence designed to understand and generate human-like text based on the input it receives. These models are trained on vast amounts of data, typically including books, articles, websites, and other textual content from the internet. This extensive training allows LLMs to perform a wide range of tasks, such as answering questions, writing essays, generating code, translating languages, and more.

Key features of large language models include:

  1. Scalability: LLMs can process and generate text at scale, handling complex and nuanced tasks with high accuracy.
  2. Adaptability: They can adapt to various domains and contexts, making them versatile tools for different applications.
  3. Contextual Understanding: LLMs can understand context and maintain coherence over long sequences of text, which is crucial for tasks like conversation and storytelling.
  4. Generalization: They can apply their knowledge to new and unseen data, enabling them to handle a wide range of inputs effectively.

LLMs have become increasingly important in fields such as natural language processing (NLP), artificial intelligence research, and various industry applications, including customer service, content creation, and educational tools.

Performance Metrics

11.02s Prediction Time
186.43s Total Time
All Input Parameters
{
  "top_p": 0.95,
  "prompt": "Give me a short introduction to large language model",
  "max_tokens": 512,
  "temperature": 0.7
}
Input Parameters
top_p Type: numberDefault: 0.95Range: 0 - 1
Controls diversity of the output. Lower values make the output more focused, higher values make it more diverse.
prompt (required) Type: string
Input text for the model
max_tokens Type: integerDefault: 512Range: 1 - ∞
Maximum number of tokens to generate
temperature Type: numberDefault: 0.7Range: 0 - 1
Controls randomness. Lower values make the model more deterministic, higher values make it more random.
Output Schema

Output

Type: arrayItems Type: string

Example Execution Logs
Total runtime: 11.018418788909912
Version Details
Version ID
3bd45c9b5baae49397bceac44f99ccada63d9709a4611eddedd4ff592e4e678f
Version Created
September 22, 2024
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