Note

• Datatype: 4-bit bnb nf4 quantised format
• Model Source
• License: Apache-2.0

Introduction

The Mistral-Nemo-Instruct model is a cutting-edge 12 billion parameter language model developed in collaboration with NVIDIA. It is designed to set a new standard for small models, featuring an extensive context length of up to 128,000 tokens. This state-of-the-art model excels in reasoning, world knowledge, and coding tasks, making it one of the most capable models within its size category. .

Mistral-Nemo-Instruct LLM

Mistral-Nemo-Instruct is built upon a standard architecture, ensuring it can be easily integrated into systems already utilizing previous models such as Mistral 7B. This model is a significant step forward in natural language processing, thanks to its large context window and multilingual capabilities. It has been fine-tuned for instruction-based tasks, demonstrating superior performance in following precise instructions, engaging in multi-turn conversations, and generating code.

Key Features:

• 12 Billion Parameters: A balance between size and performance, offering advanced capabilities in a manageable scale.
• 128K Context Length: This extended context window allows the model to handle long documents, complex instructions, and extended conversations more effectively.
• Multilingual: Trained on a diverse range of languages, including English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese, Korean, Arabic, and Hindi, making it suitable for global applications.
• Tekken Tokenizer: A highly efficient tokenizer based on Tiktoken, optimized for over 100 languages and offering superior compression, especially for source code and languages with complex scripts.

Run Mistral-Nemo Instruct with an API

You can run the Mistral-Nemo Instruct Model using Clarifai’s python SDK.

Check out the Code Below:

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

export CLARIFAI_PAT={your personal access token}

from clarifai.client.model import Model

prompt = "What’s the future of AI?"

system_prompt = "You are a AI expert."

inference_params = dict(temperature=0.2, max_tokens=100, top_p=0.95, tok_k = 40, num_beams=1, system_prompt=system_prompt)

# Model Predict
model_prediction = Model("https://clarifai.com/mistralai/completion/models/mistral-7B-Instruct").predict_by_bytes(prompt.encode(), "text", inference_params=inference_params)

You can also run Mistral-Nemo 7B API using other Clarifai Client Libraries like Java, cURL, NodeJS, PHP, etc here.

Use Cases

Mistral-Nemo-Instruct is versatile, making it suitable for a wide range of applications, including but not limited to:

• Multilingual Chatbots: Offering support for customer service in multiple languages.
• Instruction Following: Effective in scenarios requiring precise adherence to complex instructions.
• Extended Conversations: Ideal for applications that involve long-form dialogue, such as therapy bots or detailed technical support.
• Code Generation: Strong capabilities in generating and understanding code, making it useful for developers and code-assist tools.
• Knowledge Management: Handling large documents or datasets in various languages, useful for legal, educational, and research purposes.

Evaluation and Benchmark Results

Mistral-Nemo-Instruct has undergone rigorous evaluation to ensure it meets the highest standards. It shows state-of-the-art performance in its category, particularly in tasks involving:

• Reasoning: Excels in logical and analytical tasks.
• World Knowledge: Provides accurate and contextually relevant information across various subjects.
• Coding Accuracy: Outperforms other models in generating and understanding source code.
• Multilingual Proficiency: Effective across a diverse set of languages, thanks to its extensive training on multilingual datasets.

Benchmark results indicate that Mistral-Nemo-Instruct consistently outperforms similar models in the 12B parameter range, particularly in multilingual tasks and extended context handling.

Advantages

• Extended Context Length: The ability to process up to 128,000 tokens enables handling of complex tasks and long documents.
• Multilingual Proficiency: Designed to serve a global audience, with strong performance across major world languages.
• Efficient Tokenization: The Tekken tokenizer offers superior compression, reducing computational costs and improving processing efficiency, particularly for complex scripts and source code.
• Instruction Fine-tuning: Enhanced fine-tuning improves the model's ability to follow instructions accurately, engage in multi-turn conversations, and generate code.

Limitations

• Resource Requirements: Despite its efficiency, the 12B parameter model still requires significant computational resources, which may limit its deployment on smaller hardware.
• Context Length vs. Performance Trade-off: While the extended context window is a major advantage, it may come at the cost of increased latency or slower response times in certain applications.
• Language Coverage: While the model supports over 100 languages, performance may vary depending on the language, with potential limitations in languages not well-represented in the training data.