14B
Maintainer: CausalLM
291
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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 CausalLM 14B model is a large language model developed by the CausalLM team. It is fully compatible with the Meta LLaMA 2 model and can be loaded using the Transformers library without requiring external code. The model can be quantized using GGUF, GPTQ, and AWQ methods for efficient inference on various hardware.
The CausalLM 14B-DPO-alpha version has been shown to outperform the Zephyr-7b model on the MT-Bench evaluation, demonstrating strong performance compared to other models of similar size. The CausalLM 7B-DPO-alpha version also performs well on this benchmark. Both the 14B and 7B models have high consistency, so the 7B version can be used as a more efficient alternative if your hardware has insufficient VRAM.
Model inputs and outputs
Inputs
- Text prompts in the chatml format
Outputs
- Generated text continuations based on the input prompt
Capabilities
The CausalLM 14B model has demonstrated strong performance on a variety of benchmarks, including MMLU, CEval, and GSM8K, often outperforming other models of similar size. It has also achieved a high win rate on the AlpacaEval Leaderboard, indicating its effectiveness in open-ended dialogue tasks.
What can I use it for?
The CausalLM 14B model can be used for a wide range of natural language processing tasks, such as text generation, question answering, and language modeling. Its strong performance on benchmarks suggests it could be useful for applications like conversational AI, content creation, and knowledge-based systems.
Things to try
One interesting aspect of the CausalLM 14B model is its compatibility with the LLaVA1.5 prompt format, which enables rapid implementation of effective multimodal capabilities by aligning the ViT Projection module with the frozen language model under visual instructions. This could be an exciting area to explore for researchers and developers interested in building multimodal AI systems.
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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