eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v4-test

TEXT GENERATIONConcurrency Cost:1Model Size:7BQuant:FP8Ctx Length:8kPublished:Mar 9, 2024License:cc-by-nc-4.0Architecture:Transformer Open Weights Cold

The eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v4-test is a 7 billion parameter language model with an 8192 token context length. This model is a merge, indicated by "merge" in its name, suggesting it combines characteristics from multiple base models. Specific details regarding its architecture, training, and primary differentiators are not provided in the available model card, making its exact capabilities and optimal use cases currently undefined.

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Model Overview

The eren23/ogno-monarch-jaskier-merge-7b-OH-PREF-DPO-v4-test is a 7 billion parameter language model. It features an 8192 token context length, which allows for processing longer inputs and generating more extensive outputs compared to models with smaller context windows.

Key Characteristics

  • Parameter Count: 7 billion parameters, placing it in the medium-sized category for language models.
  • Context Length: 8192 tokens, enabling the model to handle substantial amounts of information within a single interaction.
  • Model Type: The name indicates it is a "merge" model, suggesting it is a composite of other models, potentially combining their strengths. However, specific details about the merged components or the merging methodology are not provided in the current model card.

Current Limitations

Based on the provided model card, detailed information regarding the following aspects is currently unavailable:

  • Developed by: Creator information is not specified.
  • Model Type: Specific architecture or base models are not detailed.
  • Language(s): Supported languages are not listed.
  • License: Licensing information is missing.
  • Training Data & Procedure: No details on the datasets used for training or the training methodology.
  • Evaluation Results: Performance metrics or benchmarks are not provided.

Users should be aware that without further information, the specific capabilities, biases, risks, and optimal use cases for this model remain to be determined.

Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

temperature
top_p
top_k
frequency_penalty
presence_penalty
repetition_penalty
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