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]
- Paper [optional]: [More Information Needed]
- 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]
[More Information Needed]
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).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
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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APA:
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Glossary [optional]
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Model Card Authors [optional]
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Framework versions
