I need you to interpret these instructions and then insert the correct code in the included script: Instructions [To use the increased context, set the Loader to ExLlama, set max_seq_len to 8192 or 4096, and set compress_pos_emb to 4 for 8192 context, or to 2 for 4096 context.] Script: [# Load model directly
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
torch.cuda.init()
torch.cuda.set_device(0)
tc= torch.cuda.is_available()
torch.cuda.current_device()
print(tc)
#Load GPU
device = torch.device(“cuda:0” if torch.cuda.is_available() else “cpu”)
#Load main function
def main():
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(“TheBloke/Nous-Hermes-13B-SuperHOT-8K-fp16”)
model = AutoModelForCausalLM.from_pretrained(“TheBloke/Nous-Hermes-13B-SuperHOT-8K-fp16”,
trust_remote_code=True
)
while True:
# Get user input
user_input = input(“You: “)
# Tokenize the input text
input_ids = tokenizer.encode(user_input, return_tensors=”pt”)
# Generate a response using the model
with torch.no_grad():
output = model.generate(input_ids, max_length=250, pad_token_id=tokenizer.eos_token_id)
# Decode and print the response
response = tokenizer.decode(output[0], skip_special_tokens=True)
print(“AI: “, response)
#run the app
if __name__ == “__main__”:
main()
To use the increased context as instructed, you need to modify the script by setting the appropriate parameters for the tokenizer and model. Here’s the updated code:
“`python
# Load model directly
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# Set the Loader to ExLlama
tokenizer = AutoTokenizer.from_pretrained(
“TheBloke/Nous-Hermes-13B-SuperHOT-8K-fp16″,
loader=”exllama”
)
# Set max_seq_len to 8192 or 4096
max_seq_len = 8192 # You can change this to 4096 for 4096 context
# Set compress_pos_emb to 4 for 8192 context, or to 2 for 4096 context
model = AutoModelForCausalLM.from_pretrained(
“TheBloke/Nous-Hermes-13B-SuperHOT-8K-fp16”,
trust_remote_code=True,
max_position_embeddings=max_seq_len // compress_pos_emb
)
# Load GPU
device = torch.device(“cuda:0” if torch.cuda.is_available() else “cpu”)
# Load main function
def main():
while True:
# Get user input
user_input = input(“You: “)
# Tokenize the input text
input_ids = tokenizer.encode(user_input, return_tensors=”pt”)
# Generate a response using the model
with torch.no_grad():
output = model.generate(input_ids, max_length=250, pad_token_id=tokenizer.eos_token_id)
# Decode and print the response
response = tokenizer.decode(output[0], skip_special_tokens=True)
print(“AI: “, response)
# Run the app
if __name__ == “__main__”:
main()
“`
Make sure you have the appropriate version of the transformers library installed that supports these parameters. The changes to the tokenizer and model instantiation should enable the increased context for the AI model.
(Bambi) C:SarahOneIrisNous-Hermes-13B-SuperHOT-8K-fp16>python app.py
╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮
│ C:SarahOneIrisNous-Hermes-13B-SuperHOT-8K-fp16app.py:2 in │
│ │
│ 1 from transformers import AutoTokenizer, pipeline, logging │
│ ❱ 2 from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig │
│ 3 import argparse │
│ 4 │
│ 5 model_name_or_path = “TheBloke/Nous-Hermes-13B-SuperHOT-8K-fp16” │
│ │
│ C:UsersSarah.condaenvsBambilibsite-packagesauto_gptq__init__.py:1 in │
│ │
│ ❱ 1 from .modeling import BaseQuantizeConfig │
│ 2 from .modeling import AutoGPTQForCausalLM │
│ 3 from .utils.peft_utils import get_gptq_peft_model │
│ 4 │
│ │
│ C:UsersSarah.condaenvsBambilibsite-packagesauto_gptqmodeling__init__.py:1 in │
│ │
│ ❱ 1 from ._base import BaseGPTQForCausalLM, BaseQuantizeConfig │
│ 2 from .auto import * │
│ 3 from .bloom import * │
│ 4 from .gpt2 import * │
│ │
│ C:UsersSarah.condaenvsBambilibsite-packagesauto_gptqmodeling_base.py:26 in │
│ │
│ 23 from ..nn_modules.qlinear import GeneralQuantLinear │
│ 24 from ..nn_modules._fused_base import FusedBaseAttentionModule, FusedBaseMLPModule │
│ 25 from ..quantization import GPTQ │
│ ❱ 26 from ..utils.data_utils import collate_data │
│ 27 from ..utils.import_utils import dynamically_import_QuantLinear, TRITON_AVAILABLE, AUTOG │
│ 28 │
│ 29 logger = getLogger(__name__) │
│ │
│ C:UsersSarah.condaenvsBambilibsite-packagesauto_gptqutilsdata_utils.py:7 in │
│ │
│ 4 from typing import Callable, Dict, List, Optional │
│ 5 │
│ 6 import torch │
│ ❱ 7 from datasets import load_dataset, DatasetDict, IterableDatasetDict │
│ 8 from torch import LongTensor │
│ 9 from torch.utils.data import DataLoader │
│ 10 from transformers import PreTrainedTokenizer │
│ │
│ C:UsersSarah.condaenvsBambilibsite-packagesdatasets__init__.py:24 in │
│ │
│ 21 │
│ 22 import platform │
│ 23 │
│ ❱ 24 import pyarrow │
│ 25 from packaging import version │
│ 26 │
│ 27 │
│ │
│ C:UsersSarah.condaenvsBambilibsite-packagespyarrow__init__.py:65 in │
│ │
│ 62 # to workaround Cython bug in https://github.com/cython/cython/issues/3603 │
│ 63 _gc_enabled = _gc.isenabled() │
│ 64 _gc.disable() │
│ ❱ 65 import pyarrow.lib as _lib │
│ 66 if _gc_enabled: │
│ 67 │ _gc.enable() │
│ 68 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
It seems like you are encountering an error related to importing the `datasets` module, specifically, there appears to be an issue with importing `pyarrow`. This might be due to a version conflict or an installation problem.
To troubleshoot this issue, you can try the following steps:
1. Check PyArrow Version: Ensure that you have the latest version of PyArrow installed in your environment. You can update it using the following command:
“`
pip install –upgrade pyarrow
“`
2. Check Datasets Version: Verify that you have the latest version of the `datasets` library installed. You can update it using the following command:
“`
pip install –upgrade datasets
“`
3. Verify Dependencies: Make sure that all the required dependencies for the `datasets` library are installed. You can install them using:
“`
pip install –upgrade requests tqdm xxhash
“`
4. Check Environment: Double-check that you are running the script in the correct environment (`Bambi`).
If the issue persists, please check your Python environment, including the installed packages and their versions, to identify any potential conflicts or problems.
Additionally, if the error still occurs after trying the above steps, please provide more information about your environment, such as the Python version and the list of installed packages, to help us further investigate the issue.
(amberr) (.env) C:SarahOneCoderengineer>pip install –upgrade torch
Requirement already satisfied: torch in f:gptq4llamaengineer.envlibsite-packages (2.0.1)
Requirement already satisfied: filelock in f:gptq4llamaengineer.envlibsite-packages (from torch) (3.12.2)
Requirement already satisfied: typing-extensions in f:gptq4llamaengineer.envlibsite-packages (from torch) (4.6.3)
Requirement already satisfied: sympy in f:gptq4llamaengineer.envlibsite-packages (from torch) (1.12)
Requirement already satisfied: networkx in f:gptq4llamaengineer.envlibsite-packages (from torch) (3.1)
Requirement already satisfied: jinja2 in f:gptq4llamaengineer.envlibsite-packages (from torch) (3.1.2)
Requirement already satisfied: MarkupSafe>=2.0 in f:gptq4llamaengineer.envlibsite-packages (from jinja2->torch) (2.1.3)
Requirement already satisfied: mpmath>=0.19 in f:gptq4llamaengineer.envlibsite-packages (from sympy->torch) (1.3.0)
(amberr) (.env) C:SarahOneCoderengineer>python app.py
Traceback (most recent call last):
File “C:SarahOneCoderengineerapp.py”, line 12, in
import torch
ModuleNotFoundError: No module named ‘torch’
It seems like the Python environment you are using (`amberr` with the virtual environment `(.env)`) has the required packages installed, including `torch`. However, when running the `app.py` script, it still encounters an error stating that the `torch` module is not found.
This issue might be caused by having multiple Python installations or virtual environments with conflicting packages. To ensure that the `app.py` script runs correctly, follow these steps:
1. Activate the Virtual Environment: Before running the script, make sure you are inside the virtual environment `(.env)` where you installed the required packages.
“`
(amberr) (.env) C:SarahOneCoderengineer>
“`
2. Verify Python Interpreter: Double-check that the Python interpreter being used within the virtual environment is the same one where you installed the packages. You can do this by running:
“`
(amberr) (.env) C:SarahOneCoderengineer>python –version
“`
Ensure that the version displayed matches the one you used to install the packages.
3. Run the Script: Run the `app.py` script again using the Python interpreter from within the virtual environment:
“`
(amberr) (.env) C:SarahOneCoderengineer>python app.py
“`
If you still encounter the same error after following these steps, it’s possible that the virtual environment is not set up correctly. In that case, you can try creating a new virtual environment, installing the required packages inside it, and then running the script within that environment.