lucataco/qwen1.5-1.8b 🔢📝 → 📝
About
Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data
Example Output
Prompt:
"Give me a short introduction to large language model."
Output
A large language model (LLM) is a type of artificial intelligence (AI) system that is designed to generate human-like text based on a large corpus of text data. These models are typically trained on a large dataset of text, such as books, articles, or web pages, and use a combination of statistical and neural network techniques to learn patterns and relationships in the text.
The main goal of an LLM is to produce text that is grammatically correct, coherent, and semantically meaningful, similar to the output of a human writer. This is achieved by using a combination of techniques such as natural language understanding (NLU), machine translation, and text generation.
The training process for an LLM involves feeding it a large corpus of text data and allowing it to learn the patterns and relationships in the text. The model is then trained on this data using a variety of algorithms, such as deep learning neural networks, to generate text that is similar to the input data. During training, the model is exposed to a large number of examples of text, and it adjusts its internal parameters to minimize the difference between its generated text and the input data.
Once the model has been trained, it can be used to generate text in a variety of contexts, such as generating product descriptions, writing news articles, or even writing creative writing. The output of an LLM can be used for a wide range of applications, from generating text for chatbots and virtual assistants to creating personalized content for websites and social media platforms.
Overall, large language models are a powerful tool for generating human-like text, and they have the potential to revolutionize a wide range of industries and applications. However, they also raise important ethical and societal questions, such as the potential for bias in the training data and the impact of large language models on employment and privacy. As such, ongoing research and development in the field of large language models is critical to ensuring that they are used in a responsible and ethical manner.
The main goal of an LLM is to produce text that is grammatically correct, coherent, and semantically meaningful, similar to the output of a human writer. This is achieved by using a combination of techniques such as natural language understanding (NLU), machine translation, and text generation.
The training process for an LLM involves feeding it a large corpus of text data and allowing it to learn the patterns and relationships in the text. The model is then trained on this data using a variety of algorithms, such as deep learning neural networks, to generate text that is similar to the input data. During training, the model is exposed to a large number of examples of text, and it adjusts its internal parameters to minimize the difference between its generated text and the input data.
Once the model has been trained, it can be used to generate text in a variety of contexts, such as generating product descriptions, writing news articles, or even writing creative writing. The output of an LLM can be used for a wide range of applications, from generating text for chatbots and virtual assistants to creating personalized content for websites and social media platforms.
Overall, large language models are a powerful tool for generating human-like text, and they have the potential to revolutionize a wide range of industries and applications. However, they also raise important ethical and societal questions, such as the potential for bias in the training data and the impact of large language models on employment and privacy. As such, ongoing research and development in the field of large language models is critical to ensuring that they are used in a responsible and ethical manner.
Performance Metrics
66.24s
Prediction Time
66.25s
Total Time
All Input Parameters
{
"top_k": 1,
"top_p": 1,
"prompt": "Give me a short introduction to large language model.",
"temperature": 1,
"system_prompt": "You are a helpful assistant.",
"max_new_tokens": 512,
"repetition_penalty": 1
}
Input Parameters
- seed
- The seed for the random number generator
- top_k
- When decoding text, samples from the top k most likely tokens; lower to ignore less likely tokens.
- top_p
- When decoding text, samples from the top p percentage of most likely tokens; lower to ignore less likely tokens.
- prompt
- Input prompt
- temperature
- Adjusts randomness of outputs, greater than 1 is random and 0 is deterministic, 0.75 is a good starting value.
- system_prompt
- System prompt
- max_new_tokens
- The maximum number of tokens to generate
- repetition_penalty
- Penalty for repeated words in generated text; 1 is no penalty, values greater than 1 discourage repetition, less than 1 encourage it.
Output Schema
Output
Example Execution Logs
Using seed: 813713259 The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results. Setting `pad_token_id` to `eos_token_id`:151645 for open-end generation.
Version Details
- Version ID
7d8bd353757fc04876c9d66e508b836972f86dabf833b67023511200a4d81509- Version Created
- February 6, 2024