Instructions to use sujitvasanth/TheBloke-openchat-3.5-0106-GPTQ-PEFTadapterJsonSear with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sujitvasanth/TheBloke-openchat-3.5-0106-GPTQ-PEFTadapterJsonSear with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/home/sujit/Downloads/text-generation-webui-main/models/TheBloke_openchat-3.5-0106-GPTQ") model = PeftModel.from_pretrained(base_model, "sujitvasanth/TheBloke-openchat-3.5-0106-GPTQ-PEFTadapterJsonSear") - Notebooks
- Google Colab
- Kaggle
Upload README.md
Browse files
README.md
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---
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library_name: peft
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base_model: sujitvasanth/TheBloke-openchat-3.5-0106-GPTQ
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---
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# Model Card for Model ID
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<!-- Finetuned version of openchat for extracting information from a database json object. -->
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## Model Details
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### Model Description
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<!-- Finetuned version of openchat for extracting information from a database json object. It is train -->
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- **Developed by:** Dr Sujit Vasanth
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- **Model type:** QLoRA PEFT
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- **Language(s) (NLP):** Json, English
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** TheBloke/openchat-3.5-0106-GPTQ
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### Model Sources [optional]
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- **Repository:** https://github.com/sujitvasanth/GPTQ-finetune
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- **Demo [optional]:** https://github.com/sujitvasanth/GPTQ-finetune/blob/main/GPTQ-finetune.py
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## How to Get Started with the Model
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model = AutoModelForCausalLM.from_pretrained(model_id,
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quantization_config= GPTQConfig(bits=4, disable_exllama=False),device_map="auto") # is_trainable=True
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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tokenizer.pad_token = tokenizer.eos_token
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model.load_adapter(adapter_id)
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## Training Details
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### Training Data
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<!-- https://huggingface.co/datasets/sujitvasanth/jsonsearch2 -->
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https://huggingface.co/datasets/sujitvasanth/jsonsearch2
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User: Assistant examples of Json search Query
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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QLora PEFT training on custom dataset
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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### Framework versions
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- PEFT 0.8.2
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