mistralai_Mistral-7B-Instruct-v0.2-testgen-LoRAs

Model Card for Model ID

LoRA weights for "mistralai_Mistral-7B-Instruct-v0.2"

Model Description

Fine-tuned model to talk like middle school students, using typos/grammar errors. Trained on student Q&As physics topics such as PE/KE/LCE including pulley/ramp examples.

• Developed by: Nora T
• Finetuned from model: mistralai_Mistral-7B-Instruct-v0.2

Additional Sources

• 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)

1. 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,
      )

Direct Use

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.

Training Hyperparams

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

Training Data

Trained on raw text file

Training Procedure

Trained using an open-source web-ui:

• https://github.com/oobabooga/text-generation-webui

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 -->

Testing Data, Factors & Metrics

Model Examination [optional]

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

Technical Specifications [optional]

Compute Infrastructure

[More Information Needed]

Hardware Requirements

[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