metadata

license: apache-2.0
tags:
  - finetuned
  - quantized
  - 4-bit
  - gptq
  - transformers
  - safetensors
  - mistral
  - text-generation
  - Safetensors
  - text-generation-inference
  - merge
  - 7b
  - mistralai/Mistral-7B-Instruct-v0.1
  - HuggingFaceH4/zephyr-7b-beta
  - pytorch
  - generated_from_trainer
  - en
  - dataset:HuggingFaceH4/ultrachat_200k
  - dataset:HuggingFaceH4/ultrafeedback_binarized
  - arxiv:2305.18290
  - arxiv:2310.16944
  - base_model:mistralai/Mistral-7B-v0.1
  - license:mit
  - model-index
  - autotrain_compatible
  - endpoints_compatible
  - has_space
  - region:us
  - license:apache-2.0
model_name: zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ
base_model: MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi

Description

MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GPTQ is a quantized (GPTQ) version of MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1

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/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-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"])