lucataco/mistrallite:0cc86161 | Run with an API on Replicate

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lucataco /mistrallite:0cc86161

Playground API

Input

Run this model in Node.js with one line of code:

npx create-replicate --model=lucataco/mistrallite

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run lucataco/mistrallite using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/mistrallite:0cc8616147b2fe24148d899688fb386ac9f3a2e19dba42c1d25732b7d4fb0f55",
  {
    input: {
      prompt: "What are the main challenges to support a long context for LLM?",
      max_new_tokens: 400
    }
  }
);

console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run lucataco/mistrallite using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/mistrallite:0cc8616147b2fe24148d899688fb386ac9f3a2e19dba42c1d25732b7d4fb0f55",
    input={
        "prompt": "What are the main challenges to support a long context for LLM?",
        "max_new_tokens": 400
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run lucataco/mistrallite using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "lucataco/mistrallite:0cc8616147b2fe24148d899688fb386ac9f3a2e19dba42c1d25732b7d4fb0f55",
    "input": {
      "prompt": "What are the main challenges to support a long context for LLM?",
      "max_new_tokens": 400
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

The main challenges to support a long context LLM include: 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. 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. 3. Model complexity: A long context LLM is likely to be a complex model, which can be difficult to train and optimize. 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. 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. 6. Ethical considerations: A long context LLM may raise ethical concerns, such as the potential for bias or the impact on privacy and security.

Generated in

10.2 seconds

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