---
library_name: peft
base_model: mistralai/Mistral-7B-Instruct-v0.2
language:
- en
pipeline_tag: text-generation
tags:
- education
---
# Base Model: mistralai/Mistral-7B-Instruct-v0_2_student_answer_train_examples_mistral_0416
  * LoRAs weights for Mistral-7b-Instruct-v0_2


# Noteworthy changes: 
  * reduced training hyperparams: epochs=3 (previously 4)
  * new training prompt: "Teenager students write in simple sentences.
          You are a teenager student, and please answer the following question. {training example}"

  * old training prompt: "Teenager students write in simple sentences [with typos and grammar errors].
          You are a teenager student, and please answer the following question. {training example}"


## Model Details
Fine-tuned model that talks like middle school students, using simple vocabulary and grammar. 
  * Trained on student Q&As physics topics including pulley/ramp examples that discuss work, force, and etc.

### Model Description

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



- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Model Details
Fine-tuned model to talk like middle school students, using typos/grammar errors. 
Trained on student Q&As physics topics including pulley/ramp examples that discuss work, force, and etc.


- **Developed by:** Nora T
- **Finetuned from model:** mistralai_Mistral-7B-Instruct-v0.2
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## How to Get Started:
1. Load Mistral model first:
```
from peft import PeftModel # for fine-tuning
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, GenerationConfig, GPTQConfig, BitsAndBytesConfig

model_name_or_path = "mistralai/Mistral-7B-Instruct-v0.2"
nf4_config = BitsAndBytesConfig( # quantization 4-bit
   load_in_4bit=True,
   bnb_4bit_quant_type="nf4",
   bnb_4bit_use_double_quant=True,
   bnb_4bit_compute_dtype=torch.bfloat16
)
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                                             device_map="auto",
                                             trust_remote_code=False,
                                             quantization_config=nf4_config,
                                             revision="main")

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
```

2. Load in LoRA weights:
```
lora_model_path = "{path_to_loras_folder}/mistralai_Mistral-7B-Instruct-v0.2-testgen-LoRAs" # load loras
model = PeftModel.from_pretrained(
        model, lora_model_path, torch_dtype=torch.float16, force_download=True,
      )

```

## Training Hyperparams
  * LoRA Rank: 128
  * LoRA Alpha: 32
  * Batch Size: 64
  * Cutoff Length: 256
  * Learning rate: 3e-4 
  * Epochs: 3
  * LoRA Dropout: 0.05

### Training Data
Trained on raw text file 

#### Preprocessing [optional]

[More Information Needed]


## Technical Specifications 

### Model Architecture and Objective

[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. -->

### Framework versions

- PEFT 0.7.1