README.md

---
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

<!-- Provide a longer summary of what this model is. -->



- **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

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

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

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **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]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[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