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---
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]
<!-- Provide the basic links for the model. -->
- **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 |