metadata

license: apache-2.0
datasets:
  - tatsu-lab/alpaca
language:
  - en
library_name: transformers

Model Card for `dlite-v1-124m`

AI Squared's dlite-v1-124m (blog post) is a large language model which is derived from OpenAI's smallest GPT-2 model and fine-tuned on a single T4 GPU on a corpus of 50k records (Stanford Alpaca) to help it exhibit chat-based capabilities.

While dlite-v1-124m is not a state-of-the-art model, we believe that the level of interactivity that can be achieved on such a small model that is trained so cheaply is important to showcase, as it continues to demonstrate that creating powerful AI capabilities may be much more accessible than previously thought.

Model Description

Developed by: AI Squared, Inc.
Shared by: AI Squared, Inc.
Model type: Large Language Model
Language(s) (NLP): EN
License: Apache v2.0
Finetuned from model: GPT-2

Bias, Risks, and Limitations

dlite-v1-124m is not a state-of-the-art language model. dlite-v1-124m is an experimental technology and is not designed for use in any environment other than for research purposes. Furthermore, the model can sometimes exhibit undesired behaviors. Some of these behaviors include, but are not limited to: factual inaccuracies, biases, offensive responses, toxicity, and hallucinations. Just as with any other LLM, we advise users of this technology to exercise good judgment when applying this technology.

Usage

The code below shows how to use dlite-v1-124m in the way which it was trained. While the model can be used "out of the box" using the transformers library, using the function defined below to create a response from the model will achieve better results.

Load Model and Tokenizer from this Repository Using the `transformers` Package

from transformers import AutoModelForCausalLM, AutoTokenizer
import numpy as np
import re

model_id = 'aisquared/dlite-v1-124m'

tokenizer = AutoTokenizer.from_pretrained(model_id, padding_side = 'left')
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code = True, device_map = 'auto')

Create the Prompt Format and Other Variables

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

### Instruction:
{instruction}

### Response:
"""

END_KEY = '### End'
RESPONSE_KEY = '### Response:\n'

Create a Function to Retrieve a Response

def create_response(
        instruction,
        model,
        tokenizer,
        do_sample = True,
        max_new_tokens = 256,
        top_p = 0.92,
        top_k = 0,
        **kwargs
):
    """
    Create a response from the model by using a formatted prompt
    """
    input_ids = tokenizer(
        PROMPT.format(instruction=instruction), return_tensors="pt"
    ).input_ids

    gen_tokens = model.generate(
        input_ids,
        pad_token_id=tokenizer.pad_token_id,
        do_sample=do_sample,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        top_k=top_k,
        **kwargs,
    )
    decoded = tokenizer.batch_decode(gen_tokens)[0]

    # The response appears after "### Response:".  The model has been trained to append "### End" at the end.
    m = re.search(r"#+\s*Response:\s*(.+?)#+\s*End", decoded, flags=re.DOTALL)

    response = None
    if m:
        response = m.group(1).strip()
    else:
        # The model might not generate the "### End" sequence before reaching the max tokens.  In this case, return
        # everything after "### Response:".
        m = re.search(r"#+\s*Response:\s*(.+)", decoded, flags=re.DOTALL)
        if m:
            response = m.group(1).strip()
        else:
            pass
    return response

Model Card for dlite-v1-124m