Herry443/Mistral-7B-KNUT-ref-en

TEXT GENERATIONConcurrency Cost:1Model Size:7BQuant:FP8Ctx Length:8kPublished:Mar 23, 2024License:cc-by-4.0Architecture:Transformer Open Weights Cold

Herry443/Mistral-7B-KNUT-ref-en is a 7 billion parameter language model based on the Mistral-7B-Instruct-v0.2 architecture. This model is fine-tuned using a combination of datasets including Open-Platypus, Databricks Dolly-15k, and Orca Math Word Problems-200k. It is designed to enhance instruction following and problem-solving capabilities, particularly in areas like mathematical reasoning and general instruction adherence. The model leverages its training on diverse instruction-tuned datasets to provide improved responses across various prompts.

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Herry443/Mistral-7B-KNUT-ref-en: Enhanced Instruction Following

Herry443/Mistral-7B-KNUT-ref-en is a 7 billion parameter language model built upon the robust Mistral-7B-Instruct-v0.2 base architecture. This model has undergone a targeted fine-tuning process to significantly improve its ability to follow instructions and solve problems.

Key Capabilities & Training

The model's enhanced performance stems from its training on a curated selection of high-quality datasets, including:

  • garage-bAInd/Open-Platypus: A dataset known for its diverse instruction-following tasks.
  • databricks/databricks-dolly-15k: Focuses on human-generated instructions across various domains.
  • microsoft/orca-math-word-problems-200k: Specifically targets mathematical reasoning and word problem-solving skills.

By sampling from these datasets, the model aims to achieve a balanced improvement in general instruction adherence and specialized reasoning tasks.

Ideal Use Cases

This model is particularly well-suited for applications requiring:

  • Improved instruction following: Generating accurate and relevant responses based on explicit instructions.
  • Mathematical problem-solving: Handling word problems and numerical reasoning tasks.
  • General-purpose conversational AI: Benefiting from a broader understanding of diverse prompts due to its varied training data.

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
min_p