Here's an improved version with detailed steps, proper markdown formatting, and clear instructions:

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Langchain-Transformers-Python

This guide walks you through setting up a Python environment, installing dependencies, configuring GPU usage, and running a transformer model with LangChain.

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1. Create a Virtual Environment

Creating a virtual environment helps isolate dependencies and prevents conflicts with other Python projects.

For Windows (Command Prompt)

python -m venv langchain-env
langchain-env\Scripts\activate

For macOS/Linux (Terminal)

python -m venv langchain-env
source langchain-env/bin/activate

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2. Install Requirements

Once the virtual environment is activated, install the required dependencies.

pip install langchain transformers langchain-huggingface

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3. Configure GPU Usage

If you have an NVIDIA GPU, install the CUDA-enabled version of PyTorch.

Run the following command (replacing cu126 with your CUDA version):

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126

To check which CUDA version you have installed, run:

nvcc --version

If you don’t have CUDA installed, follow the official installation guide:
🔗 CUDA Installation Guide

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4. Check for GPU Availability

Run the following Python code to verify that your GPU is available:

import torch

# Check if GPU is available
gpu_available = torch.cuda.is_available()
device_name = torch.cuda.get_device_name(0) if gpu_available else "No GPU found"

print(f"GPU Available: {gpu_available}")
print(f"GPU Name: {device_name}")

If torch.cuda.is_available() returns False, ensure that:

• You have an NVIDIA GPU.
• The correct version of CUDA is installed.
• You installed the CUDA-enabled version of PyTorch.

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5. Set Device in Pipeline

Once GPU availability is confirmed, specify the device in the transformer pipeline.

from transformers import pipeline

# Load the model and set device to GPU (device=0)
model = pipeline(
    "text-generation",
    model="mistralai/Mistral-7B-Instruct-v0.1",
    device=0  # Use GPU (0 refers to the first GPU)
)

# Generate text
output = model("What is LangChain?")
print(output)

If using a CPU instead of a GPU, change device=0 to device=-1.

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🎯 Summary

Step	Command / Code
Create a Virtual Env	python -m venv langchain-env && source langchain-env/bin/activate
Install Requirements	pip install langchain transformers langchain-huggingface
Install GPU Support	pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
Check GPU Availability	print(torch.cuda.is_available())
Run Model on GPU	pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.1", device=0)

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Now you’re ready to use LangChain and Transformers with GPU acceleration! 🚀