Llama-2-7b-chat-mlx
Maintainer: mlx-community
84
🛠️
| 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
Llama-2-7b-chat-mlx is a 7 billion parameter version of Meta's Llama 2 family of large language models. It has been fine-tuned for dialogue use cases and converted to the MLX format for use in Apple's ML framework. The Llama-2-7b-chat-hf and Llama-2-7b-chat models are similar dialogue-focused versions of Llama 2, but optimized for the Hugging Face Transformers library.
Model Inputs and Outputs
The Llama-2-7b-chat-mlx model takes in text input and generates text output. It is designed for conversational dialogue, with specific formatting requirements to achieve the expected chat-like performance, including the use of INST and <<SYS>> tags, BOS and EOS tokens, and whitespaces/line breaks.
Inputs
- Text input to be used as a prompt for the model
Outputs
- Generated text continuing the dialogue
Capabilities
The Llama-2-7b-chat-mlx model is capable of engaging in open-ended dialogue, answering questions, and generating human-like responses. It has been fine-tuned to perform well on tasks like commonsense reasoning, world knowledge, and reading comprehension. Compared to open-source chat models, it generally outperforms on benchmarks and in human evaluations for helpfulness and safety.
What Can I Use It For?
The Llama-2-7b-chat-mlx model is well-suited for building conversational AI assistants, chatbots, and other applications that require natural language generation and understanding. It could be used to power customer service interactions, task-oriented dialogues, or creative writing assistants.
As noted in the maintainer's description, the model is intended for commercial and research use in English, and developers should perform safety testing and tuning tailored to their specific applications before deployment.
Things to Try
One interesting aspect of the Llama 2 models is their range of scaling - from 7 billion to 70 billion parameters. You could experiment with using different size versions of the model to see how the performance and capabilities scale. The Llama Model Index provides direct links to access these other Llama 2 variations.
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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