BiLLa-7B-SFT

Maintainer: Neutralzz

Total Score

65

Last updated 5/28/2024

🔮

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

Get summaries of the top AI models delivered straight to your inbox:

Model overview

BiLLa is an open-source reasoning-enhanced bilingual LLaMA model developed by Neutralzz. It is based on the LLaMA model and aims to greatly improve Chinese language modeling while minimizing the damage to the original English abilities. The key features include full-parameter optimization, additional task data with ChatGPT-generated analysis during training, and a bilingual reasoning-enhanced model.

Similar models include the BELLE-7B-2M model, which is a Chinese language model based on Bloomz-7b1-mt and fine-tuned with 2M Chinese data combined with 50,000 pieces of English data from Stanford-Alpaca. The llama-7b-hf-transformers-4.29 model is the original LLaMA model weights converted to the latest Transformers version.

Model inputs and outputs

Inputs

  • Text prompts in a mix of English and Chinese

Outputs

  • Multilingual text responses in both English and Chinese, with enhanced reasoning capabilities

Capabilities

BiLLa is capable of engaging in multilingual conversations, understanding context, and providing thoughtful and coherent responses in both English and Chinese. It demonstrates strong reasoning abilities, allowing it to tackle complex tasks that require analytical and problem-solving skills.

What can I use it for?

BiLLa can be a valuable tool for various applications that require bilingual language understanding and generation, such as chatbots, virtual assistants, content creation, and multilingual knowledge exploration. Its enhanced reasoning capabilities make it well-suited for tasks that involve analysis, problem-solving, and decision-making.

Things to try

One interesting thing to try with BiLLa is to engage it in a mixed-language dialogue, alternating between English and Chinese prompts, and observe how it handles the contextual shifts and maintains a coherent conversation. You can also experiment with more complex prompts that require logical reasoning, such as hypothetical scenarios or open-ended questions, to see how BiLLa responds and demonstrates its analytical abilities.



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

🏋️

BELLE-7B-2M

BelleGroup

Total Score

186

BELLE-7B-2M is a 7 billion parameter language model fine-tuned by the BelleGroup on a dataset of 2 million Chinese and 50,000 English samples. It is based on the Bloomz-7b1-mt model and has good Chinese instruction understanding and response generation capabilities. The model can be easily loaded using the AutoModelForCausalLM from Transformers. Similar models include the Llama-2-13B-GGML model created by TheBloke, which is a GGML version of Meta's Llama 2 13B model. Both models are large language models trained on internet data and optimized for instructional tasks. Model inputs and outputs Inputs Text input in the format Human: {input} \n\nAssistant: Outputs Textual responses generated by the model, continuing the conversation from the provided input Capabilities The BELLE-7B-2M model demonstrates strong performance on Chinese instruction understanding and response generation tasks. It can engage in open-ended conversations, provide informative answers to questions, and assist with a variety of language-based tasks. What can I use it for? The BELLE-7B-2M model could be useful for building conversational AI assistants, chatbots, or language-based applications targeting Chinese and English users. Its robust performance on instructional tasks makes it well-suited for applications that require understanding and following user instructions. Things to try You could try prompting the BELLE-7B-2M model with open-ended questions or tasks to see the breadth of its capabilities. For example, you could ask it to summarize an article, generate creative writing, or provide step-by-step instructions for a DIY project. Experimenting with different prompts and use cases can help you better understand the model's strengths and limitations.

Read more

Updated Invalid Date

📶

Baichuan-7B-sft

hiyouga

Total Score

77

Baichuan-7B-sft is a bilingual instruction-tuned LoRA model based on the Baichuan-7B model developed by Baichuan Intelligent Technology. It was trained on instruction-following datasets including alpaca, alpaca-zh, and codealpaca, using the LLaMA-Factory training framework. Model inputs and outputs The Baichuan-7B-sft model takes text inputs and generates text outputs. It can be used for a variety of language tasks such as question answering, text generation, and code completion. Inputs Text prompts or instructions for the model to complete Outputs Relevant text responses generated by the model based on the input prompts Capabilities The Baichuan-7B-sft model is capable of following instructions and generating coherent, helpful text across a range of domains. It has shown strong performance on benchmarks like C-EVAL and MMLU, particularly for Chinese and English tasks. What can I use it for? Baichuan-7B-sft can be useful for applications that require language understanding and generation, such as chatbots, content creation assistants, and code completion tools. The model's bilingual capabilities make it well-suited for use cases involving both Chinese and English. Things to try One interesting thing to try with Baichuan-7B-sft is using it for few-shot or one-shot learning tasks, where the model is provided with limited training examples and asked to generate relevant responses. The model's strong performance on benchmarks like MMLU suggests it may excel at these types of tasks.

Read more

Updated Invalid Date

🔍

Ziya-LLaMA-13B-v1

IDEA-CCNL

Total Score

270

The Ziya-LLaMA-13B-v1 is a large-scale pre-trained language model developed by the IDEA-CCNL team. It is based on the LLaMA architecture and has 13 billion parameters. The model has been trained to perform a wide range of tasks such as translation, programming, text classification, information extraction, summarization, copywriting, common sense Q&A, and mathematical calculation. The Ziya-LLaMA-13B-v1 model has undergone three stages of training: large-scale continual pre-training (PT), multi-task supervised fine-tuning (SFT), and human feedback learning (RM, PPO). This process has enabled the model to develop robust language understanding and generation capabilities, as well as improve its reliability and safety. Similar models developed by the IDEA-CCNL team include the Ziya-LLaMA-13B-v1.1, which has further optimized the model's performance, and the Ziya-LLaMA-7B-Reward, which has been trained to provide accurate reward feedback on language model generations. Model inputs and outputs Inputs Text**: The Ziya-LLaMA-13B-v1 model can accept text input for a wide range of tasks, including translation, programming, text classification, information extraction, summarization, copywriting, common sense Q&A, and mathematical calculation. Outputs Text**: The model generates text output in response to the input, with capabilities spanning the tasks mentioned above. The quality and relevance of the output depends on the specific task and the input provided. Capabilities The Ziya-LLaMA-13B-v1 model has demonstrated impressive performance on a variety of tasks. For example, it can accurately translate between English and Chinese, generate code in response to prompts, and provide concise and informative answers to common sense questions. The model has also shown strong capabilities in tasks like text summarization and copywriting, generating coherent and relevant output. One of the model's key strengths is its ability to handle both English and Chinese input and output. This makes it a valuable tool for users and applications that require bilingual language processing capabilities. What can I use it for? The Ziya-LLaMA-13B-v1 model can be a powerful tool for a wide range of applications, from machine translation and language-based AI assistants to automated content generation and educational tools. Developers and researchers could use the model to build applications that leverage its strong language understanding and generation abilities. For example, the model could be used to develop multilingual chatbots or virtual assistants that can communicate fluently in both English and Chinese. It could also be used to create automated writing tools for tasks like copywriting, report generation, or even creative writing. Things to try One interesting aspect of the Ziya-LLaMA-13B-v1 model is its ability to perform mathematical calculations. Users could experiment with prompting the model to solve various types of math problems, from simple arithmetic to more complex equations and word problems. This could be a valuable feature for educational applications or for building AI-powered tools that can assist with mathematical reasoning. Another area to explore is the model's performance on specialized tasks, such as code generation or domain-specific language processing. By fine-tuning the model on relevant datasets, users could potentially unlock even more capabilities tailored to their specific needs. Overall, the Ziya-LLaMA-13B-v1 model represents an exciting advancement in large language models, with a versatile set of capabilities and the potential to enable a wide range of innovative applications.

Read more

Updated Invalid Date

⛏️

llama-7b-hf-transformers-4.29

elinas

Total Score

53

The llama-7b-hf-transformers-4.29 is an open-source large language model developed by the FAIR team of Meta AI. It is a 7-billion parameter model based on the transformer architecture, and is part of the larger LLaMA family of models that also includes 13B, 33B, and 65B parameter versions. The model was trained between December 2022 and February 2023 on a mix of publicly available online data, including data from sources like CCNet, C4, GitHub, Wikipedia, Books, ArXiv, and Stack Exchange. The llama-7b-hf-transformers-4.29 model was converted to work with the latest Transformers library on Hugging Face, resolving some issues with the EOS token. It is licensed under a non-commercial bespoke license, and can be used for research on large language models, including exploring potential applications, understanding model capabilities and limitations, and developing techniques to improve them. Model inputs and outputs Inputs Text prompts of arbitrary length Outputs Continuation of the input text, generating coherent and contextually relevant language Capabilities The llama-7b-hf-transformers-4.29 model exhibits strong performance on a variety of natural language understanding and generation tasks, including commonsense reasoning, reading comprehension, and question answering. It was evaluated on benchmarks like BoolQ, PIQA, SIQA, HellaSwag, WinoGrande, and others, demonstrating capabilities comparable to or better than other large language models like GPT-J. The model also shows promising results in terms of mitigating biases, with lower average bias scores across categories like gender, religion, race, and sexual orientation compared to the original LLaMA models. However, as with any large language model, the llama-7b-hf-transformers-4.29 may still exhibit biases and generate inaccurate or unsafe content, so it should be used with appropriate caution and safeguards. What can I use it for? The primary intended use of the llama-7b-hf-transformers-4.29 model is for research on large language models, such as exploring potential applications, understanding model capabilities and limitations, and developing techniques to improve them. Researchers in natural language processing, machine learning, and artificial intelligence would be the main target users for this model. While the model is not recommended for direct deployment in production applications without further risk evaluation and mitigation, it could potentially be used as a starting point for fine-tuning on specific tasks or domains, or as a general-purpose language model for prototyping and experimentation. Things to try One interesting aspect of the llama-7b-hf-transformers-4.29 model is its performance on commonsense reasoning tasks, which can provide insights into the model's understanding of the world and its ability to make inferences. Prompting the model with questions that require commonsense knowledge, such as "What is the largest animal?" or "What do you need to do to make a cake?", and analyzing its responses could be a fruitful area of exploration. Additionally, given the model's potential biases, it could be worthwhile to investigate the model's behavior on prompts related to sensitive topics, such as gender, race, or religion, and to develop techniques for mitigating these biases.

Read more

Updated Invalid Date