Llama-2-7B-Chat-GGML

Maintainer: TheBloke

Total Score

811

Last updated 5/28/2024

🤿

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

Create account to get full access

or

If you already have an account, we'll log you in

Model overview

The Llama-2-7B-Chat-GGML is a version of Meta's Llama 2 model that has been converted to the GGML format for efficient CPU and GPU inference. It is a 7 billion parameter large language model optimized for dialogue and chat use cases. The model was created by TheBloke, who has generously provided multiple quantized versions of the model to enable fast inference on a variety of hardware. This model outperforms many open-source chat models on industry benchmarks and provides a helpful and safe assistant-like conversational experience.

Similar models include the Llama-2-13B-GGML with 13 billion parameters, and the Llama-2-70B-Chat-GGUF with 70 billion parameters. These models follow a similar architecture and optimization process as the 7B version.

Model inputs and outputs

Inputs

  • Text: The model takes text prompts as input, which can include instructions, context, and conversation history.

Outputs

  • Text: The model generates coherent and contextual text responses to continue the conversation or complete the given task.

Capabilities

The Llama-2-7B-Chat-GGML model is capable of engaging in open-ended dialogue, answering questions, and assisting with a variety of tasks such as research, analysis, and creative writing. It has been optimized for safety and helpfulness, making it suitable for use as a conversational assistant.

What can I use it for?

This model could be used to power conversational AI applications, virtual assistants, or chatbots. It could also be fine-tuned for specific domains or use cases, such as customer service, education, or creative writing. The quantized GGML version enables efficient deployment on a wide range of hardware, making it accessible to developers and researchers.

Things to try

You can try using the Llama-2-7B-Chat-GGML model to engage in open-ended conversations, ask it questions on a variety of topics, or provide it with prompts to generate creative text. The model's capabilities can be explored through frameworks like text-generation-webui or llama.cpp, which support the GGML format.



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

🎲

Llama-2-13B-chat-GGML

TheBloke

Total Score

680

The Llama-2-13B-chat-GGML model is a 13-billion parameter large language model created by Meta and optimized for dialogue use cases. It is part of the Llama 2 family of models, which range in size from 7 billion to 70 billion parameters and are designed for a variety of natural language generation tasks. This specific model has been converted to the GGML format, which is designed for CPU and GPU inference using tools like llama.cpp and associated libraries and UIs. The GGML format has since been superseded by GGUF, so users are encouraged to use the GGUF versions of these models going forward. Similar models include the Llama-2-7B-Chat-GGML and the Llama-2-13B-GGML, which offer smaller and larger versions of the Llama 2 architecture in the GGML format. Model Inputs and Outputs Inputs Raw text Outputs Generated text continuations Capabilities The Llama-2-13B-chat-GGML model is capable of engaging in open-ended dialogue, answering questions, and generating coherent and context-appropriate text continuations. It has been fine-tuned to perform well on benchmarks for helpfulness and safety, making it suitable for use in assistant-like applications. What Can I Use It For? The Llama-2-13B-chat-GGML model could be used to power conversational AI assistants, chatbots, or other applications that require natural language generation and understanding. Given its strong performance on safety metrics, it may be particularly well-suited for use cases where providing helpful and trustworthy responses is important. Things to Try One interesting aspect of the Llama-2-13B-chat-GGML model is its ability to handle context and engage in multi-turn conversations. Users could try prompting the model with a series of related questions or instructions to see how it maintains coherence and builds upon previous responses. Additionally, the model's quantization options allow for tuning the balance between performance and accuracy, so users could experiment with different quantization levels to find the optimal tradeoff for their specific use case.

Read more

Updated Invalid Date

🌀

Llama-2-7B-GGML

TheBloke

Total Score

214

The Llama-2-7B-GGML is a variant of Meta's Llama 2 language model, created by the maintainer TheBloke. This 7 billion parameter model has been optimized for CPU and GPU inference using the GGML format. It is part of a collection of Llama 2 models ranging from 7 billion to 70 billion parameters, with both pretrained and fine-tuned versions available. The fine-tuned models, like this one, are optimized for dialogue use cases. Similar models include the Llama-2-13B-GGML and Llama-2-7B-Chat-GGML, which offer different parameter sizes and optimizations. Model inputs and outputs Inputs Text**: The Llama-2-7B-GGML model takes text as input. Outputs Text**: The model generates text as output. Capabilities The Llama-2-7B-GGML model is capable of a wide range of natural language generation tasks, including dialogue, summarization, and content creation. It has been shown to outperform many open-source chat models on benchmarks, and can provide helpful and safe responses on par with some popular closed-source models. What can I use it for? You can use the Llama-2-7B-GGML model for a variety of commercial and research applications, such as building AI assistants, content generation tools, and language understanding systems. The fine-tuned chat version is particularly well-suited for conversational AI use cases. Things to try Try prompting the Llama-2-7B-GGML model with open-ended questions or instructions to see its versatility in generating coherent and contextual responses. You can also experiment with different temperature and sampling settings to influence the creativity and diversity of the output.

Read more

Updated Invalid Date

🏅

Llama-2-70B-Chat-GGML

TheBloke

Total Score

160

The Llama-2-70B-Chat-GGML model is a large language model created by Meta Llama 2. It is part of the Llama 2 family of models, which range in size from 7 billion to 70 billion parameters. The model is fine-tuned for dialogue use cases, with the goal of being a helpful, respectful and honest AI assistant. Similar models in the Llama 2 family include the Llama-2-7B-Chat-GGML and Llama-2-13B-Chat-GGML models, which have smaller parameter sizes of 7 billion and 13 billion respectively. These models were also created and maintained by TheBloke. Model inputs and outputs Inputs The Llama-2-70B-Chat-GGML model takes in text input only. It can be used for a variety of natural language generation tasks. Outputs The model generates text output. It is designed to provide helpful, respectful and honest responses when used as an AI assistant. Capabilities The Llama-2-70B-Chat-GGML model has been optimized for dialogue use cases through fine-tuning. It is capable of engaging in thoughtful and nuanced conversations, providing detailed and informative responses. The model demonstrates strong commonsense reasoning and world knowledge, allowing it to address a wide range of topics. What can I use it for? This powerful 70 billion parameter model can be used for a variety of AI assistant and chatbot applications. Its strong performance on benchmark tasks makes it well-suited for use cases requiring an intelligent, knowledgeable and helpful conversational partner. Potential applications include customer service, task planning, research assistance, and creative ideation. Things to try One interesting aspect of the Llama-2-70B-Chat-GGML model is its ability to engage in extended, context-rich conversations. Prompting the model with a multi-turn dialogue can lead to increasingly coherent and relevant responses, allowing for natural back-and-forth exchanges. Experimenting with different prompting styles and leveraging the model's strong grasp of context could unlock its full potential as an interactive AI assistant.

Read more

Updated Invalid Date

📉

Llama-2-13B-GGML

TheBloke

Total Score

172

The Llama-2-13B-GGML is a 13 billion parameter language model created by Meta. It is a larger version of the Llama 2 model, which was originally released in a 7 billion parameter size. The Llama-2-13B-GGML model has been released in a GGML format, which allows for efficient CPU and GPU inference using tools like llama.cpp and associated UIs and libraries. Similar models include the WizardLM-7B-uncensored-GPTQ, a 7 billion parameter model created by Eric Hartford and optimized for GPU inference, as well as the Llama-2-7B and Llama-2-70B models from Meta, which are 7 billion and 70 billion parameter versions respectively. Model inputs and outputs The Llama-2-13B-GGML model is a text-to-text generative language model. It takes natural language text as input and generates fluent, coherent text as output. Inputs Natural language text prompts Outputs Generated natural language text Completions and continuations of the input prompts Capabilities The Llama-2-13B-GGML model is capable of tasks like open-ended conversation, question answering, summarization, and creative text generation. With its large 13 billion parameter size, it can engage in detailed, nuanced dialogue and produce high-quality, contextual outputs. What can I use it for? The Llama-2-13B-GGML model can be used for a variety of natural language processing applications, such as chatbots, virtual assistants, content generation, and language understanding. Its efficient GGML format makes it well-suited for deployment on CPUs and GPUs, allowing it to be used in a wide range of real-world scenarios. Things to try Some interesting things to try with the Llama-2-13B-GGML model include using it for creative writing tasks, where its strong language modeling capabilities can produce evocative and imaginative text. You could also experiment with fine-tuning the model on domain-specific data to adapt it for specialized applications. Additionally, exploring the model's reasoning and commonsense understanding by posing it with complex prompts or multi-step tasks could yield valuable insights.

Read more

Updated Invalid Date