lucataco / mistrallite

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

Cold

Public
650 runs
L40S
GitHub
License

Run with an API

Playground API Examples README Versions

Prediction

lucataco/mistrallite:0cc86161

Model

lucataco/mistrallite:0cc86161

njlhwkdby4od576hhdlryzspbm

Status

Succeeded

Source

Web

Hardware

A40

Total duration

12.3s

Created

over 1 year ago

Input

prompt: What are the main challenges to support a long context for LLM?
max_new_tokens: 400

{
  "prompt": "What are the main challenges to support a long context for LLM?",
  "max_new_tokens": 400
}

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": "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

Tweak it Share Report

Prediction

lucataco/mistrallite:0cc86161

Model

lucataco/mistrallite:0cc86161

5plxaadbso4m6jy32xxzjcseli

Status

Succeeded

Source

Web

Hardware

A40

Total duration

11.0s

Created

over 1 year ago

Input

prompt: please tell me how does pgvector help with Generative AI and give me some examples.
max_new_tokens: 13400

{
  "prompt": "please tell me how does pgvector help with Generative AI and give me some examples.",
  "max_new_tokens": 13400
}

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: "please tell me how does pgvector help with Generative AI and give me some examples.",
      max_new_tokens: 13400
    }
  }
);
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": "please tell me how does pgvector help with Generative AI and give me some examples.",
        "max_new_tokens": 13400
    }
)
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": "0cc8616147b2fe24148d899688fb386ac9f3a2e19dba42c1d25732b7d4fb0f55",
    "input": {
      "prompt": "please tell me how does pgvector help with Generative AI and give me some examples.",
      "max_new_tokens": 13400
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Pgvector is a library that provides a set of tools for working with large-scale vector data. It is designed to be used in conjunction with other libraries and frameworks, such as TensorFlow and PyTorch, to enable the development of scalable and efficient machine learning models. One way that Pgvector can be used in Generative AI is by providing a way to efficiently store and retrieve large amounts of vector data. This can be useful for training generative models, such as GANs (Generative Adversarial Networks), which require large amounts of data to train effectively. For example, a GAN model might be trained on a dataset of images, where each image is represented as a vector of pixel values. Pgvector can be used to store these vectors efficiently, allowing the model to access them quickly during training. Another way that Pgvector can be used in Generative AI is by providing a way to perform vector operations on large datasets. This can be useful for tasks such as image generation, where the model needs to manipulate and transform large amounts of data to generate new images. Overall, Pgvector provides a set of tools that can be used to efficiently work with large-scale vector data, which is a key component of many Generative AI models.

{
  "completed_at": "2023-10-26T03:22:59.662013Z",
  "created_at": "2023-10-26T03:22:48.701276Z",
  "data_removed": false,
  "error": null,
  "id": "5plxaadbso4m6jy32xxzjcseli",
  "input": {
    "prompt": "please tell me how does pgvector help with Generative AI and give me some examples.",
    "max_new_tokens": 13400
  },
  "logs": "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.",
  "metrics": {
    "predict_time": 11.026681,
    "total_time": 10.960737
  },
  "output": " Pgvector is a library that provides a set of tools for working with large-scale vector data. It is designed to be used in conjunction with other libraries and frameworks, such as TensorFlow and PyTorch, to enable the development of scalable and efficient machine learning models.\n\nOne way that Pgvector can be used in Generative AI is by providing a way to efficiently store and retrieve large amounts of vector data. This can be useful for training generative models, such as GANs (Generative Adversarial Networks), which require large amounts of data to train effectively.\n\nFor example, a GAN model might be trained on a dataset of images, where each image is represented as a vector of pixel values. Pgvector can be used to store these vectors efficiently, allowing the model to access them quickly during training.\n\nAnother way that Pgvector can be used in Generative AI is by providing a way to perform vector operations on large datasets. This can be useful for tasks such as image generation, where the model needs to manipulate and transform large amounts of data to generate new images.\n\nOverall, Pgvector provides a set of tools that can be used to efficiently work with large-scale vector data, which is a key component of many Generative AI models.",
  "started_at": "2023-10-26T03:22:48.635332Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/5plxaadbso4m6jy32xxzjcseli",
    "cancel": "https://api.replicate.com/v1/predictions/5plxaadbso4m6jy32xxzjcseli/cancel"
  },
  "version": "0cc8616147b2fe24148d899688fb386ac9f3a2e19dba42c1d25732b7d4fb0f55"
}

Generated in

11.0 seconds

Tweak it Share Report

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lucataco / mistrallite

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Logs (njlhwkdby4od576hhdlryzspbm)

Logs (5plxaadbso4m6jy32xxzjcseli)