TheBloke-openchat-3.5-0106-GPTQ-PEFTadapterJsonSear

Model Card for Model ID

<!-- 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.from_pretrained(model_id, quantization_config= GPTQConfig(bits=4, disable_exllama=False),device_map="auto") # is_trainable=True tokenizer = AutoTokenizer.from_pretrained(model_id) tokenizer.pad_token = tokenizer.eos_token model.load_adapter(adapter_id)

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

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QLora PEFT training on custom dataset

Preprocessing [optional]

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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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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