riteshshergill/kevin-the-ai-manager

Model Card for Model ID

Kevin is the world's first AI Manager. Expect Kevin to be on your ass 24/7, berating you for the smallest of things and crucifying you for every small mistake.

Model Details

Model Description

Need some personal time off? Kevin doesn't think so.. when there are seats to be warmed, Kevin expects you to be placed comfortably in your cubicle, looking your busiest best. Need to take time out for a funeral? Complete the last rites and get back in your chair. For Kevin, deadlines are everything!

This model is intended to be funny and not to be taken seriously or hurt anybody's sentiments. So relax and enjoy talking to Kevin.. THE WORLD'S FIRST TOXIC AI MANAGER!

• Developed by: riteshshergill
• Funded by [optional]: NA
• Shared by [optional]: NA
• Model type: INSTRUCTIONAL
• Language(s) (NLP): Python
• License: [More Information Needed]
• Finetuned from model [optional]: Mistral-7B-Instruct-v0.2

Model Sources [optional]

• Repository: [More Information Needed]
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• Demo [optional]: [More Information Needed]

Uses

Ask queries to Kevin about general office stuff like "Can I take the rest of the day off?" Expect an acerbic reply.

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig,HfArgumentParser,TrainingArguments,pipeline, logging import torch

#use quantiation to load in case of memory constraints nf4_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16 ) model_id = "mistralai/Mistral-7B-Instruct-v0.2" peft_model_id = "riteshshergill/kevin-the-ai-manager"

#device_map may be auto in case of a non gpu machine model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=nf4_config, device_map = 'cuda') tokenizer = AutoTokenizer.from_pretrained(model_id)

#load Kevin's adapters onto the base model weights model.load_adapter(peft_model_id) model.enable_adapters()

pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=60) question = "Can I leave early today?" prompt=f"[INST] {question} [/INST]" result = pipe(prompt)

Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

• Training regime:

• LORA Config:

  lora_alpha=16 lora_dropout=0.1 r=64 bias="none" task_type="CAUSAL_LM" target_modules=["q_proj", "k_proj", "v_proj", "o_proj","gate_proj"]

  num_train_epochs=3 per_device_train_batch_size=4 gradient_accumulation_steps=1 optim="paged_adamw_32bit" save_steps=5000 logging_steps=30 learning_rate=2e-4 weight_decay=0.001 fp16=False bf16=False max_grad_norm=0.3 max_steps=-1 warmup_ratio=0.03 group_by_length=True lr_scheduler_type="constant"

Speeds, Sizes, Times [optional]

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Evaluation

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

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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Glossary [optional]

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Model Card Authors [optional]

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Framework versions

• PEFT 0.10.0