CodeNinja-1.0-OpenChat-7B
Maintainer: beowolx
104
📊
| 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 CodeNinja-1.0-OpenChat-7B is an enhanced version of the renowned openchat/openchat-3.5-1210 model. It has been fine-tuned through Supervised Fine Tuning on two expansive datasets, encompassing over 400,000 coding instructions. Designed to be an indispensable tool for coders, CodeNinja aims to integrate seamlessly into your daily coding routine.
Model inputs and outputs
Inputs
- Code Prompts: CodeNinja maintains the same prompt structure as OpenChat 3.5, requiring users to adhere to the specified format for effective utilization.
Outputs
- Coded Responses: CodeNinja generates detailed code responses based on the provided prompts, drawing from its extensive training data across various programming languages.
Capabilities
CodeNinja boasts several key capabilities that make it a powerful coding assistant:
- Expansive Training Database: It has been refined with datasets from glaiveai/glaive-code-assistant-v2 and TokenBender/code_instructions_122k_alpaca_style, incorporating around 400,000 coding instructions.
- Flexibility and Scalability: Available in a 7B model size, CodeNinja is adaptable for local runtime environments.
- Advanced Code Completion: With a substantial context window size of 8192, it supports comprehensive project-level code completion.
What can I use it for?
Developers can leverage CodeNinja to streamline their coding workflows. It can assist with a variety of tasks, such as:
- Generating code snippets and entire programs based on high-level descriptions
- Providing comprehensive code completion suggestions, even for complex projects
- Translating between different programming languages
- Troubleshooting and debugging existing code
Things to try
One interesting aspect of CodeNinja is its ability to generate code across a wide range of programming languages. Try prompting it with tasks or descriptions that span different languages, such as Python, C++, and JavaScript, and observe how it handles the variations in syntax and semantics.
Another interesting experiment would be to explore the model's capabilities in terms of project-level code completion. Provide it with a partially completed codebase and see how it generates relevant code to fill in the gaps, taking into account the broader context.
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
🚀
openchat-3.5-1210
277
The openchat-3.5-1210 model is a 7B parameter AI model developed by the openchat team. It is the "Overall Best Performing Open Source 7B Model" according to the maintainers, outperforming ChatGPT (March) and Grok-1 on several benchmarks. The model is capable of both coding and general language tasks, with a 15-point improvement in Coding over the previous OpenChat-3.5 model. The openchat-3.5-0106 and openchat_3.5 are similar high-performing open-source models from the same team, with the openchat_3.5-awq and openchat-3.5-1210-gguf variants also available. All these models leverage the team's C-RLFT (Constrained Reinforcement Learning from Trajectories) fine-tuning approach to achieve exceptional results from limited training data. Model inputs and outputs Inputs Text prompts**: The model can take in text prompts from users, which can include instructions, questions, or open-ended requests. Conversation history**: The model is designed to maintain context across multiple turns of a conversation, allowing users to build upon previous exchanges. Conditional inputs**: The model supports setting a "condition" (e.g. "Code", "Math Correct") to adjust its behavior for specialized tasks. Outputs Generated text**: The primary output of the model is coherent, contextually relevant text generated in response to the input prompts. Code generation**: The model can generate code snippets when provided with appropriate programming prompts. Numeric outputs**: The model can perform basic mathematical reasoning and provide numeric outputs for problems. Capabilities The openchat-3.5-1210 model has demonstrated strong performance across a variety of benchmarks, including MT-Bench, HumanEval, and GSM8K. It outperforms both ChatGPT (March) and the proprietary Grok-1 model on several tasks, showcasing its capabilities in areas like coding, mathematical reasoning, and general language understanding. The model also supports specialized "Coding" and "Mathematical Reasoning" modes, which can be accessed by providing the appropriate conditional input. These modes allow the model to focus on more technical tasks and further enhance its capabilities in those domains. What can I use it for? The openchat-3.5-1210 model can be a valuable tool for a wide range of applications, from chatbots and virtual assistants to content generation and code development. Its strong performance on benchmarks suggests it could be useful for tasks like: Chatbots and virtual assistants**: The model's ability to maintain conversation context and generate coherent responses makes it suitable for building interactive chatbots and virtual assistants. Content generation**: The model can be used to generate creative writing, articles, and other types of text content. Code development**: The model's coding capabilities can be leveraged to assist with tasks like code generation, explanation, and debugging. Educational applications**: The model's mathematical reasoning abilities could be employed in educational tools and tutoring systems. Things to try One interesting aspect of the openchat-3.5-1210 model is its ability to adjust its behavior based on the provided "condition" input. For example, you could try prompting the model with a simple math problem and observe how it responds in the "Mathematical Reasoning" mode, compared to its more general language understanding capabilities. Additionally, the model's strong performance on coding tasks suggests it could be a valuable tool for developers. You could try providing the model with various coding challenges or prompts and see how it handles them, exploring its capabilities in areas like algorithm design, syntax generation, and code explanation.
Updated Invalid Date
🎯
opencoderplus
104
OpenCoderPlus is a series of open-source language models fine-tuned by openchat on a diverse and high-quality dataset of multi-round conversations. With only 6K GPT-4 conversations filtered from the 90K ShareGPT conversations, OpenCoderPlus is designed to achieve high performance with limited data. The model is based on the StarCoderPlus architecture and has a native 8192 context length. It achieves 102.5% of the ChatGPT score on the Vicuna GPT-4 evaluation and 78.7% win-rate on the AlpacaEval benchmark. Model inputs and outputs OpenCoderPlus is a text-to-text AI model that takes in user queries or instructions and generates relevant responses. The model uses a conversation template that involves concatenating tokens, including an end-of-turn token ` with the eot_token_id`. Inputs User questions or instructions Outputs Relevant responses generated by the model Capabilities OpenCoderPlus demonstrates strong performance on a variety of tasks, including coding, programming, and general language understanding. It outperforms ChatGPT on the Vicuna GPT-4 evaluation and achieves a high win-rate on the AlpacaEval benchmark, showcasing its capability to engage in high-level conversations and complete complex tasks. What can I use it for? OpenCoderPlus can be used for a wide range of applications, such as conversational AI assistants, code generation and completion, and knowledge-intensive tasks. The model's ability to perform well with limited training data makes it an attractive option for open-source and resource-constrained projects. Potential use cases include building AI-powered chatbots, automating software development workflows, and enhancing educational tools. Things to try One interesting aspect of OpenCoderPlus is its ability to maintain performance while using only a fraction of the training data compared to other models. This highlights the potential for open-source models to achieve strong results without requiring massive datasets. Developers and researchers may want to explore ways to further optimize the model's architecture and fine-tuning process to push the boundaries of what is possible with limited resources.
Updated Invalid Date
✅
openchat_8192
220
openchat_8192 is a series of open-source language models fine-tuned on a diverse and high-quality dataset of multi-round conversations by the openchat team. The models are based on the LLaMA-13B foundation model, with the openchat_8192 variant extending the context length to 8192 tokens. Compared to similar open-source models like OpenCoderPlus, openchat_8192 achieves higher performance despite using only ~6K fine-tuning conversations, a fraction of the data used by other models. The openchat_8192 model scored 106.6% of ChatGPT's Vicuna GPT-4 evaluation score and 79.5% of its win-rate on the AlpacaEval benchmark. Model inputs and outputs Inputs User question**: The user's input text to be processed by the model. Conversation history**: The model can accept multi-turn conversation history to provide context-aware responses. Outputs Generative text response**: The model generates a relevant and coherent response to the user's input, continuing the conversation. Capabilities The openchat_8192 model exhibits strong performance across a variety of benchmarks, demonstrating its capabilities in areas like open-ended conversation, task-oriented dialogue, and even mathematical reasoning. Despite its relatively small size compared to large language models like GPT-4, openchat_8192 can match or exceed the performance of these larger models on certain tasks. What can I use it for? The openchat_8192 model would be well-suited for building open-domain chatbots, virtual assistants, and other conversational AI applications. Its high performance on benchmarks like Vicuna GPT-4 and AlpacaEval suggests it could be used as a drop-in replacement for commercial language models in many use cases, while benefiting from the open-source and permissive licensing. Things to try One interesting aspect of the openchat_8192 model is its ability to perform well with limited training data. This could make it an attractive option for developers who want to fine-tune a language model for their specific use case but have access to only a small dataset. Experimenting with different fine-tuning strategies and dataset curation techniques could yield further performance improvements. Another area to explore is the model's capabilities in mathematical reasoning and coding tasks. The provided benchmarks show promising results, and developers could investigate integrating the openchat_8192 model into applications that require these abilities, such as programming assistants or educational tools.
Updated Invalid Date
🧠
openchat_3.5
1.1K
The openchat_3.5 model is an open-source language model developed by openchat. It is part of the OpenChat library, which aims to create high-performance, commercially viable, open-source large language models. The openchat_3.5 model is fine-tuned using a strategy called C-RLFT, which allows it to learn from mixed-quality data without preference labels. This model is capable of achieving performance on par with ChatGPT, even with a 7 billion parameter size, as demonstrated by its strong performance on the MT-bench benchmark. Similar models include the openchat_3.5-awq model and the openchat-3.5-1210-gguf model, both of which are also part of the OpenChat library and aim to push the boundaries of open-source language models. Model inputs and outputs The openchat_3.5 model is a text-to-text transformer model, capable of generating human-like text in response to input prompts. It takes natural language text as input and produces natural language text as output. Inputs Natural language text prompts Outputs Generated natural language text responses Capabilities The openchat_3.5 model is capable of a wide range of text generation tasks, including answering questions, summarizing information, and engaging in open-ended conversations. It has demonstrated strong performance on benchmark tasks, outperforming larger 70 billion parameter models in some cases. What can I use it for? The openchat_3.5 model can be used for a variety of applications, such as building chatbots, virtual assistants, and content generation tools. Its open-source nature and strong performance make it an attractive option for developers and researchers looking to leverage advanced language models in their projects. Additionally, the OpenChat team is committed to making their models commercially viable, which could open up opportunities for monetization and enterprise-level deployments. Things to try One interesting aspect of the openchat_3.5 model is its ability to learn from mixed-quality data without preference labels, thanks to the C-RLFT fine-tuning strategy. Developers could explore how this approach affects the model's performance and biases compared to more traditional fine-tuning methods. Additionally, the model's small size (7 billion parameters) compared to its strong performance could make it an attractive option for deployment on resource-constrained devices or in scenarios where model size is a concern.
Updated Invalid Date