Magicoder-S-CL-7B
Maintainer: ise-uiuc
44
🧠
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
The Magicoder-S-CL-7B model is part of the Magicoder family of models developed by Intelligent Software Engineering (iSE). It is powered by the novel OSS-Instruct approach, which empowers large language models (LLMs) with open-source code snippets to generate low-bias and high-quality instruction data for coding tasks. This helps mitigate the inherent bias of LLM-synthesized data by providing a wealth of diverse, realistic, and controllable references.
The Magicoder-S-CL-7B model was fine-tuned from the CodeLlama-7b-Python-hf model. It was trained on two datasets: the Magicoder-OSS-Instruct-75K dataset generated through OSS-Instruct, and the Magicoder-Evol-Instruct-110K dataset, which was decontaminated and redistributed from the evol-codealpaca-v1 dataset.
Model inputs and outputs
Inputs
- Coding instructions: Prompts or requests for the model to generate code or complete coding tasks.
Outputs
- Generated code: The model's response in the form of source code that aims to complete the provided coding instruction.
Capabilities
The Magicoder-S-CL-7B model is designed and best suited for coding tasks. It can generate code to solve a wide variety of programming problems, from simple tasks to more complex challenges. The model's capabilities include writing functions, implementing algorithms, and solving coding challenges across different programming languages and domains.
What can I use it for?
The Magicoder-S-CL-7B model can be used for a range of coding-related applications, such as:
- Code generation: Automatically generating code to complete programming tasks or solve coding challenges.
- Code assistance: Providing suggestions and completing partial code snippets to help developers write more efficient and effective code.
- Learning and education: Using the model as a learning tool to help students and beginners understand programming concepts and syntax.
- Prototyping and experimentation: Quickly generating code prototypes to test ideas and explore new approaches.
Things to try
One interesting thing to try with the Magicoder-S-CL-7B model is to provide it with open-ended coding challenges or prompts that require creative problem-solving. Observe how the model approaches and attempts to solve these more complex tasks, and how the generated code compares to what a human programmer might produce. This can provide valuable insights into the model's capabilities and limitations when it comes to more nuanced and open-ended coding problems.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🛠️
Magicoder-S-DS-6.7B
198
Magicoder-S-DS-6.7B is a model in the Magicoder family, developed by researchers at the University of Illinois Urbana-Champaign (UIUC). The model is empowered by a novel approach called "OSS-Instruct", which enlightens large language models (LLMs) with open-source code snippets to generate high-quality and low-bias instructional data for coding tasks. This mitigates the inherent bias of LLM-synthesized instruction data by providing a wealth of open-source references to produce more diverse, realistic, and controllable data. The Magicoder models are designed and best suited for coding tasks, and may not work as well for non-coding tasks. Similar models include codellama-13b-instruct from Meta, chatglm3-6b from nomagick, and other Llama-based models fine-tuned for coding by Meta and others. Model Inputs and Outputs Inputs Text prompts for coding-related tasks, such as code generation, code explanation, or code translation. Outputs Generated code, code explanations, or code translations, depending on the specific task. Capabilities The Magicoder-S-DS-6.7B model is capable of generating high-quality code and providing explanations for code snippets. It can be used for a variety of coding-related tasks, such as code generation, code translation, and code understanding. What Can I Use It For? The Magicoder-S-DS-6.7B model can be used for a variety of coding-related projects, such as developing intelligent code assistants, automating code generation, or enhancing code understanding. It could be particularly useful for companies looking to improve their software development workflows or for individual developers seeking to boost their coding productivity. Things to Try One interesting thing to try with the Magicoder-S-DS-6.7B model is to provide it with a coding prompt and observe how it generates code that is both syntactically correct and semantically meaningful. You could also try providing the model with a code snippet and asking it to explain the purpose and functionality of the code.
Updated Invalid Date
🤔
Magicoder-S-DS-6.7B-GGUF
75
The Magicoder-S-DS-6.7B-GGUF is a large language model created by Intellligent Software Engineering (iSE) and maintained by TheBloke. It is a 6.7B parameter model that has been quantized to the GGUF format, which offers numerous advantages over the previous GGML format. This model can be used for a variety of text-to-text tasks, including code generation, language understanding, and open-ended conversation. Similar models maintained by TheBloke include the deepseek-coder-6.7B-instruct-GGUF and the deepseek-coder-33B-instruct-GGUF, which are based on DeepSeek's Deepseek Coder models. TheBloke has also released GGUF versions of Meta's CodeLlama-7B and CodeLlama-7B-Instruct models, as well as OpenChat's openchat_3.5-7B model. Model inputs and outputs Inputs Text**: The model accepts text input, which can include natural language, code snippets, or a combination of both. Outputs Text**: The model generates text output, which can include natural language responses, code completions, or a combination of both. Capabilities The Magicoder-S-DS-6.7B-GGUF model is a versatile language model that can be used for a variety of text-to-text tasks. It has shown strong performance on benchmarks for code generation, language understanding, and open-ended conversation. For example, the model can be used to generate code snippets, answer questions about programming concepts, or engage in open-ended dialogue on a wide range of topics. What can I use it for? The Magicoder-S-DS-6.7B-GGUF model can be used for a variety of applications, such as: Code generation**: The model can be used to generate code snippets or complete programming tasks, making it a valuable tool for software developers. Language understanding**: The model can be used to understand and analyze natural language input, which can be useful for applications such as chatbots, virtual assistants, and text analysis. Open-ended conversation**: The model can be used to engage in open-ended dialogue on a wide range of topics, making it a useful tool for educational, entertainment, or customer service applications. Things to try One interesting thing to try with the Magicoder-S-DS-6.7B-GGUF model is to explore its capabilities in code generation and understanding. You could try prompting the model with a partially completed code snippet and see how it completes the task, or ask it to explain the functionality of a piece of code. Additionally, you could experiment with using the model for open-ended dialogue, exploring how it responds to a variety of conversational prompts and topics.
Updated Invalid Date
💬
WizardCoder-15B-V1.0
736
The WizardCoder-15B-V1.0 model is a large language model (LLM) developed by the WizardLM Team that has been fine-tuned specifically for coding tasks using their Evol-Instruct method. This method involves automatically generating a diverse set of code-related instructions to further train the model on instruction-following capabilities. Compared to similar open-source models like CodeGen-16B-Multi, LLaMA-33B, and StarCoder-15B, the WizardCoder-15B-V1.0 model exhibits significantly higher performance on the HumanEval benchmark, achieving a pass@1 score of 57.3 compared to the 18.3-37.8 range of the other models. Model inputs and outputs Inputs Natural language instructions**: The model takes in natural language prompts that describe coding tasks or problems to be solved. Outputs Generated code**: The model outputs code in a variety of programming languages (e.g. Python, Java, etc.) that attempts to solve the given problem or complete the requested task. Capabilities The WizardCoder-15B-V1.0 model has been specifically trained to excel at following code-related instructions and generating functional code to solve a wide range of programming problems. It is capable of tasks such as writing simple algorithms, fixing bugs in existing code, and even generating complex programs from high-level descriptions. What can I use it for? The WizardCoder-15B-V1.0 model could be a valuable tool for developers, students, and anyone working on code-related projects. Some potential use cases include: Prototyping and rapid development of new software features Automating repetitive coding tasks Helping to explain programming concepts by generating sample code Tutoring and teaching programming by providing step-by-step solutions Things to try One interesting thing to try with the WizardCoder-15B-V1.0 model is to provide it with vague or open-ended prompts and see how it interprets and responds to them. For example, you could ask it to "Write a Python program that analyzes stock market data" and see the creative and functional solutions it comes up with. Another idea is to give the model increasingly complex or challenging coding problems, like those found on programming challenge websites, and test its ability to solve them. This can help uncover the model's strengths and limitations when it comes to more advanced programming tasks.
Updated Invalid Date
🗣️
WizardCoder-33B-V1.1
124
WizardCoder-33B-V1.1 is a large language model (LLM) developed by the WizardLM team that is trained to excel at code-related tasks. It is based on the DeepSeek-Coder-33B-base model and has been further fine-tuned using the Evol-Instruct method to improve its code generation and understanding capabilities. Compared to previous versions, WizardCoder-33B-V1.1 achieves state-of-the-art performance on several industry-standard benchmarks, outperforming models like ChatGPT 3.5, Gemini Pro, and DeepSeek-Coder-33B-instruct. Model inputs and outputs Inputs Natural language instructions**: The model accepts natural language descriptions of coding tasks or problems that it should solve. Outputs Generated code**: The model's primary output is Python, Java, or other programming language code that attempts to fulfill the given instruction or solve the provided problem. Capabilities WizardCoder-33B-V1.1 demonstrates impressive abilities in generating functional code to solve a wide variety of programming tasks. It achieves 79.9 pass@1 on the HumanEval benchmark, 73.2 pass@1 on HumanEval-Plus, 78.9 pass@1 on MBPP, and 66.9 pass@1 on MBPP-Plus. These results show the model's strong performance compared to other code LLMs, making it a valuable tool for developers and programmers. What can I use it for? The WizardCoder-33B-V1.1 model can be utilized in a range of applications that involve code generation or understanding, such as: Automated code completion and suggestions to assist developers Prototyping and building initial versions of software applications Translating natural language descriptions into working code Educational tools for teaching programming concepts and skills Augmenting human programming workflows to boost productivity Things to try One interesting aspect of WizardCoder-33B-V1.1 is its ability to handle complex, multi-part instructions and generate code that addresses all the requirements. You could try providing the model with detailed prompts involving various coding tasks and see how it responds. Additionally, experimenting with different decoding strategies, such as adjusting the temperature or number of samples, may uncover further nuances in the model's capabilities.
Updated Invalid Date