RedPajama-INCITE-Chat-3B-v1-GPTQ-4bit-128g

redpajama gptq: RedPajama-INCITE-Chat-3B-v1

<a href="https://colab.research.google.com/gist/pszemraj/86d2e8485df182302646ed2c5a637059/inference-with-redpajama-incite-chat-3b-v1-gptq-4bit-128g.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a>

A GPTQ quantization of the RedPajama-INCITE-Chat-3B-v1 via auto-gptq. Model file is only 2GB.

Usage

Note that you cannot load directly from the hub with auto_gptq yet - if needed you can use this function to download using the repo name.

first install auto-GPTQ

pip install ninja auto-gptq[triton]

load:

import torch
from pathlib import Path
from auto_gptq import AutoGPTQForCausalLM
from transformers import AutoTokenizer

model_repo = Path.cwd() / "RedPajama-INCITE-Chat-3B-v1-GPTQ-4bit-128g"
device = "cuda:0" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained(model_repo)
model = AutoGPTQForCausalLM.from_quantized(
    model_repo,
    device=device,
    use_safetensors=True,
    use_triton=device != "cpu", # comment/remove if not on Linux
).to(device)

Inference:

import re
import pprint as pp


prompt = "How can I further strive to increase shareholder value even further?"
prompt = f"<human>: {prompt}\n<bot>:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    penalty_alpha=0.6,
    top_k=4,
    temperature=0.7,
    do_sample=True,
    max_new_tokens=192,
    length_penalty=0.9,
    pad_token_id=model.config.eos_token_id
)
result = tokenizer.batch_decode(
    outputs, skip_special_tokens=True, clean_up_tokenization_spaces=True
)

bot_responses = re.findall(r'<bot>:(.*?)(<human>|$)', result[0], re.DOTALL)
bot_responses = [response[0].strip() for response in bot_responses]

print(bot_responses[0])