tomasmcm / claude2-alpaca-13b

Source: umd-zhou-lab/claude2-alpaca-13B ✦ Quant: TheBloke/claude2-alpaca-13B-AWQ ✦ This model is trained by fine-tuning llama-2 with claude2 alpaca data

Cold

Public
3.9K runs
L40S
Paper
License

Run with an API

Playground API Examples README Versions

Input

prompt

*string

Shift + Return to add a new line

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
Generate a haiku about AI.

### Response:
Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
Generate a haiku about AI.

### Response:

Text prompt to send to the model.

max_tokens

integer

Maximum number of tokens to generate per output sequence.

Default: 128

presence_penalty

number

(minimum: -5, maximum: 5)

Float that penalizes new tokens based on whether they appear in the generated text so far. Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to repeat tokens.

Default: 0

frequency_penalty

number

(minimum: -5, maximum: 5)

Float that penalizes new tokens based on their frequency in the generated text so far. Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to repeat tokens.

Default: 0

temperature

number

(minimum: 0.01, maximum: 5)

Float that controls the randomness of the sampling. Lower values make the model more deterministic, while higher values make the model more random. Zero means greedy sampling.

Default: 0.8

top_p

number

(minimum: 0.01, maximum: 1)

Float that controls the cumulative probability of the top tokens to consider. Must be in (0, 1]. Set to 1 to consider all tokens.

Default: 0.95

top_k

integer

Integer that controls the number of top tokens to consider. Set to -1 to consider all tokens.

Default: -1

stop

string

Shift + Return to add a new line

List of strings that stop the generation when they are generated. The returned output will not contain the stop strings.

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

npx create-replicate --model=tomasmcm/claude2-alpaca-13b

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 tomasmcm/claude2-alpaca-13b using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "tomasmcm/claude2-alpaca-13b:49b2d4cc082625d10463f56426bd012ebd75c11e3d6c2b54f7532c6ddd46b944",
  {
    input: {
      stop: "###",
      top_k: -1,
      top_p: 0.95,
      prompt: "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\nGenerate a haiku about AI.\n\n### Response:\n",
      max_tokens: 128,
      temperature: 0.8,
      presence_penalty: 0,
      frequency_penalty: 0
    }
  }
);
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 tomasmcm/claude2-alpaca-13b using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "tomasmcm/claude2-alpaca-13b:49b2d4cc082625d10463f56426bd012ebd75c11e3d6c2b54f7532c6ddd46b944",
    input={
        "stop": "###",
        "top_k": -1,
        "top_p": 0.95,
        "prompt": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\nGenerate a haiku about AI.\n\n### Response:\n",
        "max_tokens": 128,
        "temperature": 0.8,
        "presence_penalty": 0,
        "frequency_penalty": 0
    }
)
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 tomasmcm/claude2-alpaca-13b 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": "49b2d4cc082625d10463f56426bd012ebd75c11e3d6c2b54f7532c6ddd46b944",
    "input": {
      "stop": "###",
      "top_k": -1,
      "top_p": 0.95,
      "prompt": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nGenerate a haiku about AI.\\n\\n### Response:\\n",
      "max_tokens": 128,
      "temperature": 0.8,
      "presence_penalty": 0,
      "frequency_penalty": 0
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Artificial intelligence Is a powerful technology That can help us.

Generated in

0.5 seconds

Tweak it Report

Run time and cost

This model costs approximately $0.059 to run on Replicate, or 16 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 61 seconds. The predict time for this model varies significantly based on the inputs.

Readme

Model Card for umd-zhou-lab/claude2-alpaca-13B

This model is trained by fine-tuning llama-2 with claude2 alpaca data.

Model Details

Model Description

Developed by: UMD Tianyi Zhou Lab
Model type: An auto-regressive language model based on the transformer architecture
License: Llama 2 Community License Agreement
Finetuned from model: meta-llama/Llama-2-13b

Model Sources

GitHub: Claude2-Alpaca
Data: claude2_alpaca

Uses

The primary use of this model is research on large language models and chatbots. The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.

Training

We use the prompt from Stanford Alpaca

Hyperparameter	Global Batch Size	Learning rate	Epochs	Max length	Weight decay
Model (13B)	128	1e-5	5	2048	0

Performance

Compared to the llama2-chat, our models can have better average performance.

	Average	ARC	HellaSwag	MMLU	TruthfulQA	Alpaca_Eval	Avg Length
Llama-2-7b-chat	56.335	52.9	78.55	48.32	45.57	71.37	1479
Llama-2-13b-chat	59.935	59.04	81.94	54.64	44.12	81.09	1513

claude_alpaca-7b	57.78	56.66	81.17	46.58	46.71	71.23	1066
claude_alpaca-13b	61.29	61.18	84.08	55.74	44.18	78.93	1127

Citation

Please consider citing our paper if you think our codes, data, or models are useful. Thank you!

@misc{claude2-alpaca,
  author = {Lichang Chen and Khalid Saifullah and Ming Li and Tianyi Zhou and Heng Huang},
  title = {Claude2-Alpaca: Instruction tuning datasets distilled from claude},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Lichang-Chen/claude2-alpaca}},
}