dolphin-2.9-llama3-70b
Maintainer: cognitivecomputations
67
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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
dolphin-2.9-llama3-70b is an AI model curated and trained by Eric Hartford, Lucas Atkins, Fernando Fernandes, and the community of Cognitive Computations. It is based on the Llama-3-70b model and is governed by the META LLAMA 3 COMMUNITY LICENSE AGREEMENT. The model has a 8,000 token context and was fine-tuned using 8,000 sequence length. Training took 2.5 days on an 8xH100 node provided by Crusoe Cloud.
Similar models include the dolphin-2.9-llama3-8b and dolphin-llama2-7b, both also created by Cognitive Computations. These models are built on different base Llama versions and have varying parameter counts.
Model inputs and outputs
Inputs
- Prompts: The model uses the ChatML prompt format, which includes system, user, and assistant components.
Outputs
- Text completions: The model generates continuations of the provided prompts, producing text outputs.
Capabilities
dolphin-2.9-llama3-70b has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling. The model is uncensored, meaning the dataset has been filtered to remove alignment and bias. This makes the model highly compliant, but also potentially dangerous if not used responsibly.
What can I use it for?
The dolphin-2.9-llama3-70b model can be used for a wide range of natural language processing tasks, including text generation, question answering, code generation, and more. However, due to its uncensored nature, it is important to implement appropriate safeguards and alignment layers before deploying the model in a production environment.
Things to try
One interesting aspect of dolphin-2.9-llama3-70b is its ability to engage in open-ended dialogue and handle a variety of prompts, from casual conversation to complex instructions. Experiment with providing the model with different types of prompts and observe how it responds. Additionally, you can explore the model's coding and agentic capabilities by testing it on programming-related tasks.
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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