library_name: peft
base_model: mistralai/Mistral-7B-v0.1
Model Card for Model ID
LoRA model trained for ~11 hours on r/uwaterloo data. Only trained on top-level comments with the most upvotes on each post.
Model Details
Model Description
- Developed by: Anthony Susevski and Alvin Li
- Model type: LoRA
- Language(s) (NLP): English
- License: mit
- Finetuned from model [optional]: mistralai/Mistral-7B-v0.1
Uses
Pass a post title and a post text(optional) in the style of a Reddit post into the below prompt.
prompt = f"""
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
Respond to the reddit post in the style of a University of Waterloo student.
### Input:
{post_title}
{post_text}
### Response:
Bias, Risks, and Limitations
No alignment training as of yet -- only SFT.
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 AutoTokenizer, AutoModelForCausalLM
import torch
from peft import PeftModel, PeftConfig
peft_model_id = "asusevski/mistraloo-sft"
peft_config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(peft_config.base_model_name_or_path)
model = PeftModel.from_pretrained(model, peft_model_id).to(device)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(
peft_config.base_model_name_or_path,
add_bos_token=True
)
post_title = "my example post title"
post_text = "my example post text"
prompt = f"""
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
Respond to the reddit post in the style of a University of Waterloo student.
### Input:
{post_title}
{post_text}
### Response:
"""
model_input = tokenizer(prompt, return_tensors="pt").to(device)
with torch.no_grad():
model_output = model.generate(**model_input, max_new_tokens=256, repetition_penalty=1.15)[0]
output = tokenizer.decode(model_output, skip_special_tokens=True)
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
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
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]
Framework versions
- PEFT 0.7.1