README.md

---
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
---
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">Go through <a href="https://tutorials.inferless.com/deploy-deci-7b-using-inferless">this tutorial</a>, for quickly deploy of <b>DeciLM-7B</b> using Inferless</p></div>
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# DeciLM-7B - GPTQ
- Model creator: [Upstage](https://huggingface.co./Deci)
- Original model: [DeciLM-7B](https://huggingface.co./Deci/DeciLM-7B)

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## Description

This repo contains GPTQ model files for [Deci's DeciLM-7B](https://huggingface.co./Deci/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](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co./docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code

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## Shared files, and GPTQ parameters

Models are released as sharded safetensors files.

| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co./Inferless/deciLM-7B-GPTQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co./datasets/VMware/open-instruct/viewer/) | 4096 | 5.96 GB

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## How to use
You will need the following software packages and python libraries:
```json
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 <b>app.py</b>
```python
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
```