StableBeluga2-70B-GPTQ
Maintainer: TheBloke
91
↗️
| 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
StableBeluga2-70B-GPTQ is a large language model created by Stability AI that has been quantized using GPTQ techniques by TheBloke. It is based on Stability AI's original StableBeluga2 model. TheBloke has provided multiple quantization parameter options to choose from, allowing users to balance inference quality and VRAM usage based on their hardware and needs.
Similar models include Llama-2-70B-Chat-GPTQ, Llama-2-7B-Chat-GPTQ, and Llama-2-13B-GPTQ, all of which are Llama 2 models quantized by TheBloke.
Model inputs and outputs
Inputs
- Text prompts of any length, to be completed or continued by the model.
Outputs
- Coherent, contextual text generated in response to the input prompts, of any desired length.
Capabilities
StableBeluga2-70B-GPTQ is a powerful language model capable of generating high-quality text on a wide range of topics. It can be used for tasks like creative writing, summarization, question answering, and even chatbot-like conversations. The model's large size and quantization allow for fast and efficient inference, making it suitable for real-time applications.
What can I use it for?
You can use StableBeluga2-70B-GPTQ for a variety of natural language processing tasks, such as:
- Content generation: Create original text for blog posts, articles, stories, or scripts.
- Conversation AI: Build chatbots and virtual assistants with human-like responses.
- Question answering: Develop intelligent search or query systems to answer user questions.
- Summarization: Automatically generate concise summaries of long-form text.
The model's versatility and quantization options make it a great choice for both research and commercial applications. By choosing the right quantization parameters, you can optimize the model's performance for your specific hardware and use case.
Things to try
Some interesting things to try with StableBeluga2-70B-GPTQ include:
- Experiment with different temperature and top-k/top-p settings to generate more creative or more coherent text.
- Fine-tune the model on your own dataset to specialize it for a particular domain or task.
- Combine it with other models or techniques, such as retrieval-augmented generation, to enhance its capabilities.
- Explore the model's limitations by prompting it with challenging or adversarial inputs and observing its responses.
The quantized versions of the model provided by TheBloke offer a convenient way to leverage the power of StableBeluga2 without the full memory requirements of the original. By trying out the various quantization options, you can find the right balance of performance and efficiency for your needs.
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-2-70B-GPTQ
81
The Llama-2-70B-GPTQ is a large language model created by Meta Llama 2 and quantized using GPTQ techniques by TheBloke. This model is a version of Meta's 70 billion parameter Llama 2 model that has been optimized for smaller file size and faster inference, while maintaining strong performance. TheBloke has provided several GPTQ parameter configurations to allow users to balance tradeoffs between model size, inference speed, and accuracy. Other similar models provided by TheBloke include the Llama-2-13B-GPTQ and the Llama-2-7B-Chat-GPTQ, which apply the same GPTQ quantization techniques to smaller Llama 2 model sizes. All of these models leverage Meta's publicly released Llama 2 foundation. Model inputs and outputs The Llama-2-70B-GPTQ model is an autoregressive language model that takes text as input and generates additional text as output. It can be used for a variety of natural language processing tasks such as text generation, question answering, and open-ended conversation. Inputs Text prompts**: The model accepts text prompts as input, which can be of arbitrary length. Outputs Generated text**: The model outputs additional text, continuing the input prompt. The length of the generated text can be controlled via parameters like max_new_tokens. Capabilities The Llama-2-70B-GPTQ model exhibits strong natural language understanding and generation capabilities across a wide range of domains. It performs well on benchmarks evaluating commonsense reasoning, world knowledge, reading comprehension, and mathematical reasoning. Compared to earlier versions of Llama, the 70B model in particular shows significant improvements in these areas. In addition, the fine-tuned Llama-2-Chat versions demonstrate impressive performance on conversational tasks, outperforming many open-source chatbots while approaching the capabilities of closed-source assistants like ChatGPT. What can I use it for? The Llama-2-70B-GPTQ model can be used for a wide variety of natural language processing tasks. Some potential use cases include: Content generation**: Generating coherent and contextually relevant text for applications like creative writing, article/blog post creation, and scriptwriting. Question answering**: Answering open-ended questions by drawing upon the model's broad knowledge base. Dialogue systems**: Building conversational AI assistants for customer service, task planning, and open-ended discussion. Language learning**: Using the model's language understanding capabilities to aid in language learning and education. TheBloke's GPTQ-quantized versions of the Llama 2 models, including the Llama-2-70B-GPTQ, provide a balance of performance and efficiency that makes them well-suited for deployment in production environments with limited compute resources. Things to try One interesting aspect of the Llama-2-70B-GPTQ model is the range of quantization configurations provided by TheBloke. Users can experiment with different bit depths, group sizes, and activation order settings to find the optimal balance of model size, inference speed, and accuracy for their specific use case and hardware. This flexibility allows the model to be tailored to a wide variety of deployment scenarios. Another potential area of exploration is fine-tuning the base Llama 2 model on specialized datasets to further enhance its capabilities in domains like technical writing, legal analysis, or medical diagnosis. The modular nature of these large language models makes them well-suited for continued training and adaptation.
Updated Invalid Date
👁️
Llama-2-70B-Chat-GPTQ
257
The Llama-2-70B-Chat-GPTQ is a large language model (LLM) created by TheBloke, a prominent AI model developer. This model is based on Meta's Llama 2 70B-chat, which is optimized for dialogue use cases. TheBloke has generated several GPTQ model variations with different quantization parameters to allow users to choose the best option for their hardware and performance requirements. Similar models created by TheBloke include the Llama-2-7B-Chat-GGML, Llama-2-13B-Chat-GGML, and Llama-2-13B-Chat-GGUF. These models provide a range of size and performance tradeoffs for users to consider. Model inputs and outputs Inputs Text prompts Outputs Generated text responses Capabilities The Llama-2-70B-Chat-GPTQ model is capable of engaging in open-ended dialogue and assisting with a variety of natural language processing tasks. It has been fine-tuned by Meta to excel at chat-style interactions, outperforming many open-source chatbots. The model can provide helpful, respectful and honest responses, while ensuring outputs are socially unbiased and positive. What can I use it for? The Llama-2-70B-Chat-GPTQ model can be used for a wide range of applications that require natural language understanding and generation, such as virtual assistants, chatbots, content creation, and language-based interfaces. Developers can leverage the model's strong performance in dialogue to build engaging AI-powered chat experiences for their users. Things to try One interesting aspect of the Llama-2-70B-Chat-GPTQ model is the availability of different GPTQ quantization parameter options. Users can experiment with these variations to find the best balance of model size, RAM usage, and inference performance for their specific hardware and use case. For example, the q4_K_M variant may offer a good tradeoff between quality and resource requirements for many applications.
Updated Invalid Date
➖
Llama-2-7B-GPTQ
79
The Llama-2-7B-GPTQ model is a quantized version of Meta's Llama 2 7B foundation model, created by maintainer TheBloke. This model has been optimized for GPU inference using the GPTQ (Quantization for Language Models) algorithm, providing a compressed model with reduced memory footprint while maintaining high performance. TheBloke offers multiple GPTQ parameter permutations to allow users to choose the best balance of quality and resource usage for their hardware and requirements. Similar models include the Llama-2-70B-GPTQ, Llama-2-7B-Chat-GPTQ, Llama-2-13B-GPTQ, and Llama-2-70B-Chat-GPTQ, all of which provide quantized versions of the Llama 2 models at different scales. Model inputs and outputs Inputs Text prompts provided as input for the model to generate a response. Outputs Generated text, which can be of variable length depending on the input prompt and model configuration. Capabilities The Llama-2-7B-GPTQ model can be used for a variety of natural language processing tasks, such as text generation, summarization, and question answering. It maintains the core capabilities of the original Llama 2 7B model while providing a more efficient and compact representation for GPU-based inference. What can I use it for? The Llama-2-7B-GPTQ model can be a valuable asset for developers and researchers working on projects that require high-performance text generation. Some potential use cases include: Building conversational AI assistants Generating creative content like stories, articles, or poetry Summarizing long-form text Answering questions based on provided information By leveraging the quantized model, users can benefit from reduced memory usage and faster inference speeds, making it easier to deploy the model in resource-constrained environments or real-time applications. Things to try One interesting aspect of the Llama-2-7B-GPTQ model is the variety of GPTQ parameter configurations provided by TheBloke. Users can experiment with different bit sizes, group sizes, and activation order settings to find the optimal balance between model size, inference speed, and output quality for their specific use case. This flexibility allows for fine-tuning the model to best match the hardware constraints and performance requirements of the target application. Another area to explore is the compatibility of the various GPTQ models with different inference frameworks and hardware accelerators. Testing the models across a range of platforms can help identify the most suitable deployment options for different environments and workloads.
Updated Invalid Date
🚀
Llama-2-7B-Chat-GPTQ
250
The Llama-2-7B-Chat-GPTQ is a 7 billion parameter language model created by Meta Llama 2 and made available by TheBloke. It is a quantized version of the larger Llama 2 7B Chat model, optimized for efficient inference on GPUs. TheBloke provides multiple GPTQ parameter variations to choose from, allowing users to balance model quality and resource usage based on their hardware. Similar quantized models are also available for the Llama 2 13B and 70B Chat versions. Model inputs and outputs Inputs Text prompts Outputs Continued text generation based on the input prompt Capabilities The Llama-2-7B-Chat-GPTQ model is capable of generating human-like text in response to prompts, making it well-suited for conversational AI, content creation, and language understanding tasks. It demonstrates strong performance on a variety of benchmarks, including commonsense reasoning, world knowledge, and reading comprehension. Additionally, the fine-tuned chat version has been optimized for safety and helpfulness, aiming to produce responses that are socially unbiased and avoid harmful content. What can I use it for? The Llama-2-7B-Chat-GPTQ model can be used for a wide range of natural language processing applications, such as chatbots, content generation, and language understanding. The quantized versions provided by TheBloke allow for efficient deployment on GPU hardware, making it accessible for a variety of use cases and deployment environments. Things to try One interesting aspect of the Llama-2-7B-Chat-GPTQ model is the range of quantization options available. Users can experiment with different bit depths and group sizes to find the best balance of performance and resource usage for their specific needs. Additionally, the model's fine-tuning for safety and helpfulness makes it an intriguing choice for conversational AI applications where responsible and ethical behavior is a priority.
Updated Invalid Date