# deepseek-coder-33b-instruct-gguf

Maintainer: kcaverly

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## Model overview

`deepseek-coder-33b-instruct`

is a 33B parameter model from Deepseek that has been initialized from the `deepseek-coder-33b-base`

model and fine-tuned on 2B tokens of instruction data. It is part of the Deepseek Coder series of code language models, each trained from scratch on 2 trillion tokens with 87% code and 13% natural language data in English and Chinese. The Deepseek Coder models come in a range of sizes from 1B to 33B parameters, allowing users to choose the most suitable setup for their needs. The models demonstrate state-of-the-art performance on various code-related benchmarks, leveraging a large training corpus and techniques like a 16K window size and fill-in-the-blank tasks to support project-level code completion and infilling.

## Model inputs and outputs

The `deepseek-coder-33b-instruct`

model takes a prompt as input and generates text as output. The prompt can be a natural language instruction or a mix of code and text. The model is designed to assist with a variety of coding-related tasks, from generating code snippets to completing and enhancing existing code.

### Inputs

**Prompt**: The text prompt provided to the model, which can include natural language instructions, code fragments, or a combination of both.**Temperature**: A parameter that controls the "warmth" or randomness of the model's output. Higher values lead to more creative and diverse responses, while lower values result in more conservative and coherent output.**Repeat Penalty**: A parameter that discourages the model from repeating itself too often, helping to generate more varied and dynamic responses.**Max New Tokens**: The maximum number of new tokens the model should generate in response to the input prompt.**System Prompt**: An optional prompt that can be used to set the overall behavior and role of the model, guiding it to respond in a specific way (e.g., as a programming assistant).

### Outputs

**Generated Text**: The text generated by the model in response to the input prompt, which can include code snippets, explanations, or a mix of both.

## Capabilities

The `deepseek-coder-33b-instruct`

model is capable of a wide range of coding-related tasks, such as:

**Code Generation**: Given a natural language prompt or a partial code snippet, the model can generate complete code solutions in a variety of programming languages.**Code Completion**: The model can autocomplete and extend existing code fragments, suggesting the most relevant and appropriate next steps.**Code Explanation**: The model can provide explanations and insights about code, helping users understand the logic and syntax.**Code Refactoring**: The model can suggest improvements and optimizations to existing code, making it more efficient, readable, and maintainable.**Code Translation**: The model can translate code between different programming languages, enabling cross-platform development and compatibility.

## What can I use it for?

The `deepseek-coder-33b-instruct`

model can be a valuable tool for a wide range of software development and engineering tasks. Developers can use it to speed up their coding workflows, generate prototype solutions, and explore new ideas more efficiently. Educators can leverage the model to help students learn programming concepts and techniques. Researchers can utilize the model's capabilities to automate certain aspects of their work, such as code generation and analysis.

Some specific use cases for the `deepseek-coder-33b-instruct`

model include:

**Rapid Prototyping**: Quickly generate working code samples and prototypes to explore new ideas or prove concepts.**Code Assistance**: Enhance developer productivity by providing intelligent code completion, suggestions, and explanations.**Educational Tools**: Create interactive coding exercises, tutorials, and learning resources to help students learn programming.**Automated Code Generation**: Generate boilerplate code or entire solutions for specific use cases, reducing manual effort.**Code Refactoring and Optimization**: Identify opportunities to improve the quality, efficiency, and maintainability of existing codebases.

## Things to try

One interesting aspect of the `deepseek-coder-33b-instruct`

model is its ability to generate code that can be directly integrated into larger projects. By fine-tuning the model on a specific codebase or domain, users can create a highly specialized assistant that can seamlessly contribute to their ongoing development efforts.

Another interesting use case is to leverage the model's natural language understanding capabilities to create interactive coding environments, where users can communicate with the model in plain English to explain their requirements, and the model can respond with the appropriate code solutions.

Lastly, the model's versatility extends beyond just code generation - users can also explore its potential for tasks like code refactoring, optimization, and even translation between programming languages. This opens up new possibilities for improving the quality and maintainability of software systems.

This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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### deepseek-math-7b-instruct

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deepseek-math-7b-instruct is an AI model developed by DeepSeek AI that aims to push the limits of mathematical reasoning in open language models. It is an instruct-tuned version of the base deepseek-math-7b-base model, which was initialized with the deepseek-coder-7b-base-v1.5 model and then further pre-trained on math-related tokens from Common Crawl, along with natural language and code data. The base model has achieved an impressive 51.7% score on the competition-level MATH benchmark, approaching the performance of Gemini-Ultra and GPT-4 without relying on external toolkits or voting techniques. The instruct model and the RL model built on top of the base model further improve its mathematical problem-solving capabilities. Model inputs and outputs Inputs text**: The input text, which can be a mathematical question or problem statement. For example: "what is the integral of x^2 from 0 to 2? Please reason step by step, and put your final answer within \boxed{}." top_k**: The number of highest probability vocabulary tokens to keep for top-k-filtering. top_p**: If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation. temperature**: The value used to modulate the next token probabilities. max_new_tokens**: The maximum number of tokens to generate, ignoring the number of tokens in the prompt. Outputs The model generates a text response that provides a step-by-step solution and final answer to the input mathematical problem. Capabilities The deepseek-math-7b-instruct model is capable of solving a wide range of mathematical problems, from basic arithmetic to advanced calculus and linear algebra. It can provide detailed, step-by-step reasoning and solutions without relying on external tools or resources. The model has also demonstrated strong performance on other benchmarks, such as natural language understanding, reasoning, and programming. It can be used for tasks like answering math-related questions, generating proofs and derivations, and even writing code to solve mathematical problems. What can I use it for? The deepseek-math-7b-instruct model can be useful for a variety of applications, including: Educational tools**: The model can be integrated into educational platforms or tutoring systems to provide personalized, step-by-step math instruction and feedback to students. Research and academic work**: Researchers and academics working in fields like mathematics, physics, or engineering can use the model to assist with problem-solving, proof generation, and other math-related tasks. Business and finance**: The model can be used to automate the analysis of financial data, perform risk assessments, and support decision-making in various business domains. AI and ML development**: The model's strong mathematical reasoning capabilities can be leveraged to build more robust and capable AI systems, particularly in domains that require advanced mathematical modeling and problem-solving. Things to try Some ideas for things to try with the deepseek-math-7b-instruct model include: Posing a variety of mathematical problems, from basic arithmetic to advanced calculus and linear algebra, and observing the model's step-by-step reasoning and solutions. Exploring the model's performance on different mathematical benchmarks and datasets, and comparing it to other state-of-the-art models. Integrating the model into educational or research tools to enhance mathematical learning and problem-solving capabilities. Experimenting with different input parameters, such as top_k, top_p, and temperature, to observe their impact on the model's outputs. Investigating the model's ability to generate proofs, derivations, and other mathematical artifacts beyond just problem-solving.

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### deepseek-math-7b-base

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deepseek-math-7b-base is a large language model (LLM) developed by DeepSeek AI, a leading AI research company. The model is part of the DeepSeekMath series, which focuses on pushing the limits of mathematical reasoning in open language models. The base model is initialized with DeepSeek-Coder-v1.5 7B and continues pre-training on math-related tokens from Common Crawl, natural language, and code data for a total of 500B tokens. This model has achieved an impressive score of 51.7% on the competition-level MATH benchmark, approaching the performance of Gemini-Ultra and GPT-4 without relying on external toolkits or voting techniques. The DeepSeekMath series also includes instructed (deepseek-math-7b-instruct) and reinforcement learning (deepseek-math-7b-rl) variants, which demonstrate even stronger mathematical capabilities. The instructed model is derived from the base model with further mathematical training, while the RL model is trained on top of the instructed model using a novel Group Relative Policy Optimization (GRPO) algorithm. Model inputs and outputs Inputs text**: The input text to be processed by the model, such as a mathematical problem or a natural language prompt. top_k**: The number of highest probability vocabulary tokens to keep for top-k-filtering during text generation. top_p**: If set to a float less than 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation. temperature**: The value used to modulate the next token probabilities during text generation. max_new_tokens**: The maximum number of new tokens to generate, ignoring the number of tokens in the prompt. Outputs The model outputs a sequence of generated text, which can be a step-by-step solution to a mathematical problem, a natural language response to a prompt, or a combination of both. Capabilities The deepseek-math-7b-base model demonstrates superior mathematical reasoning capabilities, outperforming existing open-source base models by more than 10% on the competition-level MATH dataset through few-shot chain-of-thought prompting. It also shows strong tool use ability, leveraging its foundations in DeepSeek-Coder-Base-7B-v1.5 to effectively solve and prove mathematical problems by writing programs. Additionally, the model achieves comparable performance to DeepSeek-Coder-Base-7B-v1.5 in natural language reasoning and coding tasks. What can I use it for? The deepseek-math-7b-base model, along with its instructed and RL variants, can be used for a wide range of applications that require advanced mathematical reasoning and problem-solving abilities. Some potential use cases include: Educational tools**: The model can be used to develop interactive math tutoring systems, homework assistants, or exam preparation tools. Scientific research**: Researchers in fields like physics, engineering, or finance can leverage the model's mathematical capabilities to aid in problem-solving, data analysis, and theorem proving. AI-powered productivity tools**: The model's ability to generate step-by-step solutions and write programs can be integrated into productivity tools to boost efficiency in various mathematical and technical tasks. Conversational AI**: The model's natural language understanding and generation capabilities can be used to build advanced chatbots and virtual assistants that can engage in meaningful mathematical discussions. Things to try One interesting aspect of the deepseek-math-7b-base model is its ability to tackle mathematical problems using a combination of step-by-step reasoning and tool use. Users can experiment with prompts that require the model to not only solve a problem but also explain its reasoning and, if necessary, write code to aid in the solution. This can help users better understand the model's unique approach to mathematical problem-solving. Additionally, users can explore the model's performance on a diverse range of mathematical domains, from algebra and calculus to probability and statistics, to gain insights into its strengths and limitations. Comparing the model's outputs with those of human experts or other AI systems can also yield valuable insights.

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### deepseek-coder-33b-instruct

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deepseek-coder-33b-instruct is a 33B parameter AI model developed by DeepSeek AI that is specialized for coding tasks. The model is composed of a series of code language models, each trained from scratch on 2T tokens with a composition of 87% code and 13% natural language in both English and Chinese. DeepSeek Coder offers various model sizes ranging from 1B to 33B parameters, enabling users to choose the setup best suited for their needs. The 33B version has been fine-tuned on 2B tokens of instruction data to enhance its coding capabilities. Similar models include StarCoder2-15B, a 15B parameter model trained on 600+ programming languages, and StarCoder, a 15.5B parameter model trained on 80+ programming languages. Model inputs and outputs Inputs Free-form natural language instructions for coding tasks Outputs Relevant code snippets or completions in response to the input instructions Capabilities deepseek-coder-33b-instruct has demonstrated state-of-the-art performance on a range of coding benchmarks, including HumanEval, MultiPL-E, MBPP, DS-1000, and APPS. The model's advanced code completion capabilities are enabled by a large 16K context window and a fill-in-the-blank training task, allowing it to handle project-level coding tasks. What can I use it for? deepseek-coder-33b-instruct can be used for a variety of coding-related tasks, such as: Generating code snippets or completing partially written code based on natural language instructions Assisting with refactoring, debugging, or improving existing code Aiding in the development of new software applications by providing helpful code suggestions and insights The flexibility of the model's different size versions allows users to choose the most suitable setup for their specific needs and resources. Things to try One interesting aspect of deepseek-coder-33b-instruct is its ability to handle both English and Chinese inputs, making it a versatile tool for developers working in multilingual environments. You could try providing the model with instructions or prompts in both languages and observe how it responds. Another interesting avenue to explore is the model's performance on more complex, multi-step coding tasks. By carefully crafting prompts that require the model to write, test, and refine code, you can push the boundaries of its capabilities and gain deeper insights into its strengths and limitations.

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### deepseek-vl-7b-base

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DeepSeek-VL is an open-source Vision-Language (VL) model designed for real-world vision and language understanding applications. Developed by the team at DeepSeek AI, the model possesses general multimodal understanding capabilities, allowing it to process logical diagrams, web pages, formula recognition, scientific literature, natural images, and even embodied intelligence in complex scenarios. Similar models include moondream2, a small vision language model designed for edge devices, llava-13b, a large language and vision model with GPT-4 level capabilities, and phi-3-mini-4k-instruct, a lightweight, state-of-the-art open model trained with the Phi-3 datasets. Model inputs and outputs The DeepSeek-VL model accepts a variety of inputs, including images, text prompts, and conversations. It can generate responses that combine visual and language understanding, making it suitable for a wide range of applications. Inputs Image**: An image URL or file that the model will analyze and incorporate into its response. Prompt**: A text prompt that provides context or instructions for the model to follow. Max New Tokens**: The maximum number of new tokens the model should generate in its response. Outputs Response**: A generated response that combines the model's visual and language understanding to address the provided input. Capabilities The DeepSeek-VL model excels at tasks that require multimodal reasoning, such as image captioning, visual question answering, and document understanding. It can analyze complex scenes, recognize logical diagrams, and extract information from scientific literature. The model's versatility makes it suitable for a variety of real-world applications. What can I use it for? DeepSeek-VL can be used for a wide range of applications that require vision-language understanding, such as: Visual question answering**: Answering questions about the content and context of an image. Image captioning**: Generating detailed descriptions of images. Multimodal document understanding**: Extracting information from documents that combine text and images, such as scientific papers or technical manuals. Logical diagram understanding**: Analyzing and understanding the content and structure of logical diagrams, such as those used in engineering or mathematics. Things to try Experiment with the DeepSeek-VL model by providing it with a diverse range of inputs, such as images of different scenes, diagrams, or scientific documents. Observe how the model combines its visual and language understanding to generate relevant and informative responses. Additionally, try using the model in different contexts, such as educational or industrial applications, to explore its versatility and potential use cases.

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