The Writer LLM service enables you to customize and use the Writer LLMs outlined below.
|Palmyra Small||128m||HuggingFace||Apache-2.0||Improving language understanding by generative pre-training – arxiv|
|Palmyra 3B||3B||HuggingFace||Apache-2.0||Improving language understanding by generative pre-training – arxiv|
|Palmyra Base||5B||HuggingFace||Apache-2.0||Improving language understanding by generative pre-training – arxiv|
|Palmyra Large||20B||HuggingFace||Apache-2.0||Improving language understanding by generative pre-training – arxiv|
|PalmyraMed||20B||HuggingFace||Apache-2.0||Palmyra-Med: Instruction-Based Fine-Tuning of LLMs Enhancing Medical Domain Performance|
|InstructPalmyra||30B||API, Writer Platform||Enterprise License||Training language models to follow instructions with human feedback|
|Palmyra-R||30B||API, Writer Platform||Enterprise License||Autoregressive language model with Retrieval-Augmented Generation|
|Palmyra-E||30B||API, Writer Platform||Enterprise License||Autoregressive language model|
|Silk Road||--||--||Enterprise License||+85K Context Length|
|Palmyra-X||43B||API, Writer Platform, On-premises||Enterprise License||Becoming self-instruct: introducing early stopping criteria for minimal instruct tuning|
|Palmyra-X||43B||API||Beta||32K context window|
|PalmyraMed||40B||API, Writer Platform, On-premises||Enterprise License||Palmyra-Med: Instruction-Based Fine-Tuning of LLMs Enhancing Medical Domain Performance|
These large language models have been pre-trained on a massive amount of Internet text. Pre-training involves taking a mathematical model with random mathematical parameters (weights) and adjusting those weights iteratively in response to discrepancies between the model's output and a comparison point indicating the expected output. The most common training method for large language models is next-word prediction over massive amounts of text.
Palmyra Small is the fastest of Writer’s LLMs and can perform important tasks such as text parsing, simple classification, address correction, and keyword recognition. Providing more context drives better performance.
Good at: Text parsing, simple classification, address correction, and keyword recognition
Palmyra Base is extremely powerful as well as incredibly fast. This model excels at many nuanced tasks such as sentiment classification and summarization. Palmyra Base is also effective as a general service chatbot, answering questions and performing Q&A.
Competent in: complex classification, text sentiment, and summarization
Camel-5b is a trained large language model that follows instructions. Based on Palmyra-Base is trained on ~70k instruction & response fine tuning records generated by Writer Team from the InstructGPT paper, including brainstorming, classification, closed quality assurance, generation, information extraction, open quality assurance, and summarization.
Palmyra Large is the most capable model family, capable of performing any task that the other models can, often with less instruction. Palmyra Large is good at comprehending the text's intent, solving logic problems, and explaining character motivations.
Good at: Few-shots, cause and effect, and audience summarization
InstructPalmyra is the most capable model. It can perform any tasks that the other models are able to, often with higher quality, longer output, and better instruction-following.
Good at: Zero-shots, cause and effect
Palmyra-R models are a general-purpose fine-tuning recipe for retrieval-augmented generation, combining pre-trained parametric and non-parametric memory for language generation.
It is more capable than the GPT-3 and GPT-3.5 models, able to perform more complex tasks, and comes in three flavors: General, Healthcare, and Fintech. It is available on-premise or via an API.
Same capabilities as the Palmyra-E mode but with ~80K context length. still in early testing stage
Updated 19 days ago