metadata

license: apache-2.0
tags:
  - finetuned
  - quantized
  - 4-bit
  - gptq
  - transformers
  - safetensors
  - llama
  - text-generation
  - dataset:ai2_arc
  - dataset:unalignment/spicy-3.1
  - dataset:codeparrot/apps
  - dataset:facebook/belebele
  - dataset:boolq
  - dataset:jondurbin/cinematika-v0.1
  - dataset:drop
  - dataset:lmsys/lmsys-chat-1m
  - dataset:TIGER-Lab/MathInstruct
  - dataset:cais/mmlu
  - dataset:Muennighoff/natural-instructions
  - dataset:openbookqa
  - dataset:piqa
  - dataset:Vezora/Tested-22k-Python-Alpaca
  - dataset:cakiki/rosetta-code
  - dataset:Open-Orca/SlimOrca
  - dataset:spider
  - dataset:squad_v2
  - dataset:migtissera/Synthia-v1.3
  - dataset:datasets/winogrande
  - dataset:nvidia/HelpSteer
  - dataset:Intel/orca_dpo_pairs
  - dataset:unalignment/toxic-dpo-v0.1
  - dataset:jondurbin/truthy-dpo-v0.1
  - dataset:allenai/ultrafeedback_binarized_cleaned
  - dataset:Squish42/bluemoon-fandom-1-1-rp-cleaned
  - dataset:LDJnr/Capybara
  - dataset:JULIELab/EmoBank
  - dataset:kingbri/PIPPA-shareGPT
  - license:other
  - autotrain_compatible
  - endpoints_compatible
  - text-generation-inference
  - region:us
  - has_space
model_name: UNA-34Beagles-32K-bf16-v1-GPTQ
base_model: one-man-army/UNA-34Beagles-32K-bf16-v1
inference: false
model_creator: one-man-army
pipeline_tag: text-generation
quantized_by: MaziyarPanahi

Description

MaziyarPanahi/UNA-34Beagles-32K-bf16-v1-GPTQ is a quantized (GPTQ) version of one-man-army/UNA-34Beagles-32K-bf16-v1

How to use

Install the necessary packages

pip install --upgrade accelerate auto-gptq transformers

Example Python code

from transformers import AutoTokenizer, pipeline
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
import torch

model_id = "MaziyarPanahi/UNA-34Beagles-32K-bf16-v1-GPTQ"

quantize_config = BaseQuantizeConfig(
        bits=4,
        group_size=128,
        desc_act=False
    )

model = AutoGPTQForCausalLM.from_quantized(
        model_id,
        use_safetensors=True,
        device="cuda:0",
        quantize_config=quantize_config)

tokenizer = AutoTokenizer.from_pretrained(model_id)

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.95,
    repetition_penalty=1.1
)

outputs = pipe("What is a large language model?")
print(outputs[0]["generated_text"])