34b-beta
Maintainer: CausalLM
56
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| Property | Value |
|---|---|
| Model Link | View 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 34b-beta model is a large language model created by CausalLM. It is a 34 billion parameter model that is designed for text-to-text generation tasks. The model builds on the capabilities of other large language models like the CausalLM 7B and CausalLM 14B versions, which have demonstrated strong performance on a variety of benchmarks.
Model inputs and outputs
Inputs
- The model accepts natural language prompts in the chatml format.
- The model can take prompts of varying lengths, though there are some precision issues with longer sequences that will be addressed in future updates.
Outputs
- The model generates human-like text continuations of the provided prompts.
- The outputs can be used for a wide range of text-to-text generation tasks, such as content creation, question answering, and dialogue.
Capabilities
The 34b-beta model has shown strong performance on a variety of benchmarks, including MMLU where it achieved an average accuracy of 63.82%, outperforming many smaller models. It has also performed well on the CEval and GSM8K benchmarks. Additionally, the model has demonstrated a high win rate of 88.26% on the AlpacaEval leaderboard, suggesting it has strong conversational and task-completion abilities.
What can I use it for?
The 34b-beta model can be used for a wide range of text-to-text generation tasks, such as content creation, question answering, dialogue, and more. Given its strong performance on benchmarks, it could be a valuable tool for companies or individuals working on language-based applications or services. However, it's important to note that the model was trained on unfiltered internet data, so users will need to carefully monitor the outputs for any objectionable content.
Things to try
One interesting aspect of the 34b-beta model is its potential for multimodal capabilities. The model was fine-tuned on the prompt format introduced in LLaVA1.5, which is unrelated to image attention calculation. This suggests that the model may be able to effectively integrate visual information, opening up possibilities for tasks like image captioning or visual question answering. Users interested in exploring these capabilities should consider aligning the ViT Projection module with the frozen language model.
Additionally, the model's strong performance on the MMLU and CEval benchmarks indicates that it could be a useful tool for knowledge-intensive tasks, such as question answering or fact-checking. Users may want to experiment with prompts that leverage the model's broad base of knowledge.
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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