3 min to read
The installation and execution of Stable Code 3B on a Windows system necessitate a systematic approach, encompassing environment configuration, dependency management, and model execution.
Stable Code 3B, an advanced autoregressive transformer model engineered by Stability AI, is explicitly optimized for code generation and completion across 18 programming languages, including Python, Java, and C++.
It consists of 2.7 billion parameters and employs a decoder-only architecture akin to Meta’s LLaMA, facilitating sophisticated contextual inference.
Prior to initializing the installation, ensure the system satisfies the following conditions:
pip
installed and up to date.Complete the setup and verify the installation using:
python --version
Utilize the following command to install requisite libraries:
pip install torch transformers huggingface-hub
Authenticate with Hugging Face:
huggingface-cli login
Proceed with model acquisition:
mkdir stable-code-3b
huggingface-cli download stabilityai/stable-code-3b --local-dir stable-code-3b --local-dir-use-symlinks False
Deploy a development environment by utilizing an IDE such as VSCode or PyCharm, ensuring seamless script execution.
Create run_stable_code.py
and incorporate the following implementation:
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Initialize tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("stabilityai/stable-code-3b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("stabilityai/stable-code-3b", trust_remote_code=True)
# Deploy model to GPU if available
if torch.cuda.is_available():
model.cuda()
# Define input sequence
input_text = "def hello_world():\n print('Hello, world!')"
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
# Generate response
tokens = model.generate(inputs['input_ids'], max_new_tokens=48, temperature=0.2)
output = tokenizer.decode(tokens[0], skip_special_tokens=True)
print(output)
Navigate to the script’s directory and execute:
python run_stable_code.py
This will yield code generation output based on the provided input prompt.
from flask import Flask, jsonify
app = Flask(__name__)
@app.route('/api/greet', methods=['GET'])
def greet():
return jsonify({"message": "Hello, World!"})
if __name__ == '__main__':
app.run(debug=True)
input_text = "Construct an SQL query to retrieve all users aged over 30."
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
tokens = model.generate(inputs['input_ids'], max_new_tokens=100)
sql_query = tokenizer.decode(tokens[0], skip_special_tokens=True)
print(sql_query)
import pandas as pd
data = {"Name": ["Alice", "Bob", "Charlie"], "Age": [25, 30, 35]}
df = pd.DataFrame(data)
print(df[df["Age"] > 28])
huggingface-cli
or confirm network stability.Dependency Conflicts: Ensure pip
is updated by executing:
pip install --upgrade pip
The model's advanced code-generation capabilities facilitate enhanced productivity and seamless integration into diverse software development workflows. Further exploration of its potential can yield significant advancements in automated programming assistance and large-scale code analysis.
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