Edit model card

Model Card for Model ID

Model Details

Model Description

  • 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]

  • Repository: [More Information Needed]
  • Paper [optional]: [More Information Needed]
  • Demo [optional]: [More Information Needed]

Uses

#code


#testing and loading model

import torch, gc
gc.collect()
torch.cuda.empty_cache()

import numpy as np
import pandas as pd
import os
from tqdm import tqdm
import bitsandbytes as bnb
import torch
import torch.nn as nn
import transformers
from datasets import Dataset
from peft import LoraConfig, PeftConfig
from trl import SFTTrainer
from transformers import (AutoModelForCausalLM,
                          AutoTokenizer,
                          BitsAndBytesConfig,
                          TrainingArguments,
                          pipeline,
                          logging)
from sklearn.metrics import (accuracy_score,
                             classification_report,
                             confusion_matrix)
from sklearn.model_selection import train_test_split

from datasets import load_dataset
from peft import LoraConfig, PeftModel

device_map = {"": 0}
PEFT_MODEL = "kr-manish/Llama-2-7b-chat-finetune-for-textGeneration"
#model_name = "NousResearch/Llama-2-7b-hf"

config = PeftConfig.from_pretrained(PEFT_MODEL)

model = AutoModelForCausalLM.from_pretrained(
    config.base_model_name_or_path,
    low_cpu_mem_usage=True,
    return_dict=True,
    #quantization_config=bnb_config,
    device_map="auto",
    #trust_remote_code=True,
    torch_dtype=torch.float16,
)

tokenizer=AutoTokenizer.from_pretrained(config.base_model_name_or_path)
tokenizer.pad_token = tokenizer.eos_token

load_model = PeftModel.from_pretrained(model, PEFT_MODEL)

test1 ="How to own a plane in the United States?"
prompt_test = test1
pipe_test = pipeline(task="text-generation",
                model=load_model,
                tokenizer=tokenizer,
                #max_length =20,
                max_new_tokens =25,
                temperature = 0.0,
                
                )
result_test = pipe_test(prompt_test)
#answer = result[0]['generated_text'].split("=")[-1]
answer_test = result_test[0]['generated_text']
answer_test

#How to own a plane in the United States?\n\nIn the United States, owning a plane is a significant investment and requires careful planning and research. Here are

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

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.

[More Information Needed]

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

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[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 0.10.0
Downloads last month
0
Unable to determine this model’s pipeline type. Check the docs .

Adapter for