Introduction

CodeLlama-70b-Instruct is a state-of-the-art large language model (LLM) specialized in code synthesis and understanding. As a part of the Code Llama series, it represents the pinnacle of the series with 70 billion parameters, optimized for processing and generating code based on natural language instructions.

CodeLlama-70B-Instruct Model

CodeLlama-70B-Instruct is a variant of the Code Llama models family, with 70 billion parameters, built on the Llama-2 architecture. It has been trained on a diverse set of coding languages and contexts, including Python, C++, Java, PHP, TypeScript, C#, and Bash. The model excels in code synthesis, understanding, completion, and debugging, capable of handling tasks based on both code and natural language prompts.

Run CodeLlama-70B-Instruct with an API

Running the API with Clarifai's Python SDK

You can run the CodeLlama-70B Instruct Model API using Clarifai’s Python SDK.

Export your PAT as an environment variable. Then, import and initialize the API Client.

Find your PAT in your security settings.

export CLARIFAI_PAT={your personal access token}

from clarifai.client.model import Model

prompt = '''<s>Source: system\n\n You are expert programmer in C++ <step> Source: user\n\n write code for factorial of n <step> Source: assistant\nDestination: user\n\n '''

inference_params = dict(temperature=0.2, max_tokens=100, top_k=40)

# Model Predict
model_prediction = Model("https://clarifai.com/meta/Llama-2/models/codeLlama-70b-Instruct").predict_by_bytes(prompt.encode(), input_type="text", inference_params=inference_params)

print(model_prediction.outputs[0].data.text.raw)

You can also run CodeLlama-70B-Instruct API using other Clarifai Client Libraries like Java, cURL, NodeJS, PHP, etc here.

Aliases: codellama, code llama, codellama-70b, codellama 70b, etc

Prompt Template

CodeLlama-70B Instruct uses a different format for the chat prompt than previous Llama 2 or CodeLlama models

'<s>Source: system\n\n {SYSTEM PROMPT} <step> Source: user\n\n {PROMPT 1} <step> Source: assistant\n\n {Assistent Response} <step> Source: user\n\n {PROMPT 2} <step> Source: assistant\nDestination: user\n\n '

Use Cases

CodeLlama-70b-Instruct is designed for a variety of applications, including:

• Code generation from natural language descriptions.
• Debugging and code completion.
• Educational tools for learning programming.
• Assisting in software development and reducing development time.
• Natural language to code conversion for numerous programming languages

Dataset

The CodeLlama-70b-Instruct model was trained on a massive 1TB token dataset comprising code and code-related data. This extensive training has equipped the model with a deep understanding of various coding paradigms and styles.

Evaluation

The model's performance was benchmarked using HumanEval and Mostly Basic Python Programming (MBPP). CodeLlama-70b-Instruct achieved impressive results, outperforming open-source, code-specific LLMs and matching the performance of models like ChatGPT:

• HumanEval: 67.8%
• MBPP: 62.2%

Advantages

• State-of-the-art performance in code tasks.
• Instruct-tuning makes it more aligned with natural language instructions.
• Supports a wide array of popular programming languages.
• Suitable for both research and commercial use.
• Reduces development time and enhances code quality.

Limitations

• While it has high performance, it may still generate incorrect or suboptimal code, necessitating human review.
• There are inherent risks in generating code, including the potential for unintentional generation of harmful or malicious code, although safety measures have been taken to mitigate this.

Disclaimer

Please be advised that this model utilizes wrapped Artificial Intelligence (AI) provided by TogetherAI (the "Vendor"). These AI models may collect, process, and store data as part of their operations. By using our website and accessing these AI models, you hereby consent to the data practices of the Vendor. We do not have control over the data collection, processing, and storage practices of the Vendor. Therefore, we cannot be held responsible or liable for any data handling practices, data loss, or breaches that may occur. It is your responsibility to review the privacy policies and terms of service of the Vendor to understand their data practices. You can access the Vendor's privacy policy and terms of service at https://www.togetherai.com/legal/privacy-policy.

We disclaim all liability with respect to the actions or omissions of the Vendor, and we encourage you to exercise caution and to ensure that you are comfortable with these practices before utilizing the AI models hosted on our site.