nateraw/llama-2-7b-paraphrase-v1 – Run with an API on Replicate

nateraw / llama-2-7b-paraphrase-v1

Cold

Public
113 runs
L40S

Run with an API

Playground API Examples README Versions

Input

prompt

*string

Shift + Return to add a new line

The capital of France is Paris
The capital of France is Paris

Prompt to send to the model.

max_new_tokens

integer

(minimum: 1)

Maximum number of tokens to generate. A word is generally 2-3 tokens

Default: 128

min_new_tokens

integer

(minimum: -1)

Minimum number of tokens to generate. To disable, set to -1. A word is generally 2-3 tokens.

Default: -1

temperature

number

(minimum: 0.01, maximum: 5)

Adjusts randomness of outputs, greater than 1 is random and 0 is deterministic, 0.75 is a good starting value.

Default: 0.7

top_p

number

(minimum: 0, maximum: 1)

When decoding text, samples from the top p percentage of most likely tokens; lower to ignore less likely tokens

Default: 0.95

repetition_penalty

number

(minimum: 0)

A parameter that controls how repetitive text can be. Lower means more repetitive, while higher means less repetitive. Set to 1.0 to disable.

Default: 1.15

stop_sequences

string

Shift + Return to add a new line

A comma-separated list of sequences to stop generation at. For example, '<end>,<stop>' will stop generation at the first instance of 'end' or '<stop>'.

seed

integer

Random seed. Leave blank to randomize the seed

debug

boolean

provide debugging output in logs

Default: false

return_logits

boolean

if set, only return logits for the first token. only useful for testing, etc.

Default: false

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

npx create-replicate --model=nateraw/llama-2-7b-paraphrase-v1

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 nateraw/llama-2-7b-paraphrase-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "nateraw/llama-2-7b-paraphrase-v1:17b76fbd699fcd4476b6d7292de8bfd1ee1b219f7ed81f0395da0631d00850cc",
  {
    input: {
      debug: false,
      top_p: 0.9,
      prompt: "The capital of France is Paris\n",
      temperature: 0.75,
      return_logits: false,
      max_new_tokens: 128,
      min_new_tokens: -1,
      repetition_penalty: 1.15
    }
  }
);
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 nateraw/llama-2-7b-paraphrase-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "nateraw/llama-2-7b-paraphrase-v1:17b76fbd699fcd4476b6d7292de8bfd1ee1b219f7ed81f0395da0631d00850cc",
    input={
        "debug": False,
        "top_p": 0.9,
        "prompt": "The capital of France is Paris\n",
        "temperature": 0.75,
        "return_logits": False,
        "max_new_tokens": 128,
        "min_new_tokens": -1,
        "repetition_penalty": 1.15
    }
)

# The nateraw/llama-2-7b-paraphrase-v1 model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
    # https://replicate.com/nateraw/llama-2-7b-paraphrase-v1/api#output-schema
    print(item, end="")

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 nateraw/llama-2-7b-paraphrase-v1 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": "17b76fbd699fcd4476b6d7292de8bfd1ee1b219f7ed81f0395da0631d00850cc",
    "input": {
      "debug": false,
      "top_p": 0.9,
      "prompt": "The capital of France is Paris\\n",
      "temperature": 0.75,
      "return_logits": false,
      "max_new_tokens": 128,
      "min_new_tokens": -1,
      "repetition_penalty": 1.15
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

The capital of France is located in Paris.

{
  "completed_at": "2023-11-14T23:28:48.136324Z",
  "created_at": "2023-11-14T23:28:35.938654Z",
  "data_removed": false,
  "error": null,
  "id": "uuys223ba4qjmni5ejfz6u5fjq",
  "input": {
    "debug": false,
    "top_k": 50,
    "top_p": 0.9,
    "prompt": "The capital of France is Paris\n",
    "temperature": 0.75,
    "max_new_tokens": 128,
    "min_new_tokens": -1
  },
  "logs": "Your formatted prompt is:\nThe capital of France is Paris\nprevious weights were different, switching to https://replicate.delivery/pbxt/TVlCb4hSte1nUqEMOrIjrcnitL1WlK3l5vOzb99Voz2fOb4RA/training_output.zip\nDownloading peft weights\nusing https://replicate.delivery/pbxt/TVlCb4hSte1nUqEMOrIjrcnitL1WlK3l5vOzb99Voz2fOb4RA/training_output.zip instead of https://replicate.delivery/pbxt/TVlCb4hSte1nUqEMOrIjrcnitL1WlK3l5vOzb99Voz2fOb4RA/training_output.zip\nDownloaded training_output.zip as 10 824 kB chunks in 0.3440 with 0 retries\nDownloaded peft weights in 0.344\nUnzipped peft weights in 0.010\nInitialized peft model in 0.006\nOverall initialize_peft took 9.265\nExllama: False\nINFO 11-14 23:28:47 async_llm_engine.py:371] Received request 0: prompt: 'The capital of France is Paris\\n', sampling params: SamplingParams(n=1, best_of=1, presence_penalty=0.0, frequency_penalty=1.0, temperature=0.75, top_p=0.9, top_k=50, use_beam_search=False, length_penalty=1.0, early_stopping=False, stop=['</s>'], ignore_eos=False, max_tokens=128, logprobs=None, skip_special_tokens=True), prompt token ids: None.\nINFO 11-14 23:28:47 llm_engine.py:631] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%\nINFO 11-14 23:28:48 async_llm_engine.py:111] Finished request 0.\nhostname: model-hs-73001d65-da72d39bf79629ac-gpu-a40-85ddd9ccc9-97bzw",
  "metrics": {
    "predict_time": 9.586471,
    "total_time": 12.19767
  },
  "output": [
    "The",
    " capital",
    " of",
    " France",
    " is",
    " located",
    " in",
    " Paris",
    ".",
    ""
  ],
  "started_at": "2023-11-14T23:28:38.549853Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/uuys223ba4qjmni5ejfz6u5fjq",
    "cancel": "https://api.replicate.com/v1/predictions/uuys223ba4qjmni5ejfz6u5fjq/cancel"
  },
  "version": "17b76fbd699fcd4476b6d7292de8bfd1ee1b219f7ed81f0395da0631d00850cc"
}

Generated in

9.6 seconds

Tweak it Report

Run time and cost

This model costs approximately $0.075 to run on Replicate, or 13 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 77 seconds. The predict time for this model varies significantly based on the inputs.

Readme

This model doesn't have a readme.