asusevski/mistraloo-sft

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

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Software

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Citation [optional]

BibTeX:

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APA:

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Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

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Model Card Contact

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Framework versions

• PEFT 0.7.1