Nous-Hermes-Llama2-GPTQ
Maintainer: TheBloke
58
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| 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 |
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Model overview
The Nous-Hermes-Llama2-GPTQ is a large language model created by NousResearch and quantized using GPTQ techniques by TheBloke. This model is based on the Nous Hermes Llama 2 13B, which was fine-tuned on over 300,000 instructions from diverse datasets. The quantized GPTQ version provides options for different bit sizes and quantization parameters to balance performance and resource requirements.
Similar models include the Nous-Hermes-13B-GPTQ and the Nous-Hermes-Llama2-GGML, which offer different formats and quantization approaches for the same underlying Nous Hermes Llama 2 model.
Model inputs and outputs
Inputs
- The model takes in raw text as input, following the Alpaca prompt format:
### Instruction:
<prompt>
### Response:
Outputs
- The model generates text in response to the given prompt, in a natural language format.
- The output can range from short, concise responses to longer, more detailed text.
Capabilities
The Nous-Hermes-Llama2-GPTQ model is capable of a wide range of language tasks, from creative writing to following complex instructions. It stands out for its long responses, low hallucination rate, and absence of censorship mechanisms. The model was fine-tuned on a diverse dataset of over 300,000 instructions, enabling it to perform well on a variety of benchmarks.
What can I use it for?
You can use the Nous-Hermes-Llama2-GPTQ model for a variety of natural language processing tasks, such as:
- Creative writing: Generate original stories, poems, or descriptions based on prompts.
- Task completion: Follow complex instructions and complete tasks like coding, analysis, or research.
- Conversational AI: Develop chatbots or virtual assistants that can engage in natural, open-ended dialogue.
The quantized GPTQ versions of the model also make it more accessible for deployment on a wider range of hardware, from local machines to cloud-based servers.
Things to try
One interesting aspect of the Nous-Hermes-Llama2-GPTQ model is the availability of different quantization options, each with its own trade-offs in terms of performance, accuracy, and resource requirements. You can experiment with the various GPTQ versions to find the best balance for your specific use case and hardware constraints.
Additionally, you can explore the model's capabilities by trying a variety of prompts, from creative writing exercises to complex problem-solving tasks. Pay attention to the model's ability to maintain coherence, avoid hallucination, and provide detailed, informative responses.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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