MistralLiteA is a fine-tuned Mistral-7B-v0.1 language model, with enhanced capabilities of processing long context (up to 32K tokens)

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  • 619 runs

Run mistrallite with an API

Use one of our client libraries to get started quickly. Clicking on a library will take you to the Playground tab where you can tweak different inputs, see the results, and copy the corresponding code to use in your own project.

Input schema

The fields you can use to run this model with an API. If you don't give a value for a field its default value will be used.

Field Type Default value Description
What are the main challenges to support a long context for LLM?
Input prompt

Max: 16000

Max new tokens

Output schema

The shape of the response you’ll get when you run this model with an API.

{'title': 'Output', 'type': 'string'}
Example API response
View prediction
(' The main challenges to support a long context LLM include:\n'
 '1. Data availability: A long context LLM requires a large amount of data to '
 'train on, which can be difficult and expensive to collect and annotate.\n'
 '2. Computational resources: Training a long context LLM requires a lot of '
 'computational resources, including powerful hardware and software, which can '
 'be costly and difficult to obtain.\n'
 '3. Model complexity: A long context LLM is likely to be a complex model, '
 'which can be difficult to train and optimize.\n'
 '4. Evaluation: Evaluating the performance of a long context LLM can be '
 'challenging, as it may not be clear what metrics to use or how to interpret '
 'the results.\n'
 '5. Human evaluation: A long context LLM may produce outputs that are '
 'difficult for humans to understand or interpret, which can make it difficult '
 "to evaluate the model's performance.\n"
 '6. Ethical considerations: A long context LLM may raise ethical concerns, '
 'such as the potential for bias or the impact on privacy and security.')