metadata

base_model: Deci/DeciLM-7B
inference: false
language:
  - en
license: apache-2.0
model-index:
  - name: DeciLM-7B
    results: []
model_creator: Deci
model_name: DeciLM-7B
model_type: deci
prompt_template: |
  <|im_start|>system
  {system_message}<|im_end|>
  <|im_start|>user
  {prompt}<|im_end|>
  <|im_start|>assistant
quantized_by: Inferless
tags:
  - finetune
  - vllm
  - GPTQ
  - Deci
pipeline_tag: text-generation

Serverless GPUs to scale your machine learning inference without any hassle of managing servers, deploy complicated and custom models with ease.

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Go through this tutorial, for quickly deploy of DeciLM-7B using Inferless

DeciLM-7B - GPTQ

Model creator: Deci
Original model: DeciLM-7B

Description

This repo contains GPTQ model files for Deci's DeciLM-7B.

About GPTQ

GPTQ is a method that compresses the model size and accelerates inference by quantizing weights based on a calibration dataset, aiming to minimize mean squared error in a single post-quantization step. GPTQ achieves both memory efficiency and faster inference.

It is supported by:

Text Generation Webui - using Loader: AutoAWQ
vLLM - version 0.2.2 or later for support for all model types.
Hugging Face Text Generation Inference (TGI)
Transformers version 4.35.0 and later, from any code or client that supports Transformers
AutoAWQ - for use from Python code

Shared files, and GPTQ parameters

Models are released as sharded safetensors files.

Branch	Bits	GS	AWQ Dataset	Seq Len	Size
main	4	128	VMware Open Instruct	4096	5.96 GB

How to use

You will need the following software packages and python libraries:

build:
  cuda_version: "12.1.1"
  system_packages:
    - "libssl-dev"
  python_packages:
    - "torch==2.1.2"
    - "vllm==0.2.6"
    - "transformers==4.36.2"
    - "accelerate==0.25.0"

Here is the code for app.py

from vllm import LLM, SamplingParams

class InferlessPythonModel:
    def initialize(self):

        self.sampling_params = SamplingParams(temperature=0.7, top_p=0.95,max_tokens=256)
        self.llm = LLM(model="Inferless/deciLM-7B-GPTQ", quantization="gptq", dtype="float16")

    def infer(self, inputs):
        prompts = inputs["prompt"]
        result = self.llm.generate(prompts, self.sampling_params)
        result_output = [[[output.outputs[0].text,output.outputs[0].token_ids] for output in result]

        return {'generated_result': result_output[0]}

    def finalize(self):
        pass