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sujitvasanth/TheBloke-openchat-3.5-0106-GPTQ-PEFTadapterJsonSear

sourceHugging Faceupdated 3y agoView on Hugging Face
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<!-- Finetuned version of openchat for extracting information from a database json object. -->

Model Details

Model Description

<!-- Finetuned version of openchat for extracting information from a database json object. It is train -->

  • —Developed by: Dr Sujit Vasanth
  • —Model type: QLoRA PEFT
  • —Language(s) (NLP): Json, English
  • —License: [More Information Needed]
  • —Finetuned from model [optional]: TheBloke/openchat-3.5-0106-GPTQ

Model Sources [optional]

  • —Repository: https://github.com/sujitvasanth/GPTQ-finetune
  • —Demo [optional]: https://github.com/sujitvasanth/GPTQ-finetune/blob/main/GPTQ-finetune.py

How to Get Started with the Model

model = AutoModelForCausalLM.frompretrained(modelid, quantizationconfig= GPTQConfig(bits=4, disableexllama=False),devicemap="auto") # istrainable=True tokenizer = AutoTokenizer.frompretrained(modelid) tokenizer.padtoken = tokenizer.eostoken model.loadadapter(adapterid)

Training Details

Training Data

<!-- https://huggingface.co/datasets/sujitvasanth/jsonsearch2 --> https://huggingface.co/datasets/sujitvasanth/jsonsearch2 User: Assistant examples of Json search Query

Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> QLora PEFT training on custom dataset

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters
  • —Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
Speeds, Sizes, Times [optional]

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary
Hardware

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Software

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Citation [optional]

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BibTeX:

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APA:

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Glossary [optional]

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Model Card Authors [optional]

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Framework versions

  • —PEFT 0.8.2