bboeun/sft-mistral7b-base-hh-2

TEXT GENERATIONConcurrency Cost:1Model Size:7BQuant:FP8Ctx Length:4kPublished:Mar 2, 2026Architecture:Transformer Cold

The bboeun/sft-mistral7b-base-hh-2 is a 7 billion parameter language model based on the Mistral architecture. This model is a fine-tuned version, likely optimized for specific conversational or instruction-following tasks, building upon a base Mistral 7B model. Its 4096-token context length supports processing moderately long inputs for various natural language understanding and generation applications. It is intended for general-purpose text generation and understanding, with a focus on improved human-like interaction through supervised fine-tuning.

Loading preview...

Model Overview

The bboeun/sft-mistral7b-base-hh-2 is a 7 billion parameter language model, likely derived from the Mistral 7B base architecture. This model has undergone supervised fine-tuning (SFT), indicating an optimization process to enhance its performance on specific tasks, typically involving instruction following or conversational abilities. The model's name suggests it builds upon a base Mistral 7B model and incorporates elements related to 'hh' which often refers to human feedback or helpfulness alignment.

Key Characteristics

  • Architecture: Based on the efficient Mistral 7B architecture.
  • Parameter Count: 7 billion parameters, offering a balance between performance and computational efficiency.
  • Context Length: Supports a context window of 4096 tokens, suitable for processing and generating moderately long texts.
  • Fine-tuning: Implies supervised fine-tuning for improved instruction adherence and conversational quality.

Potential Use Cases

Given its fine-tuned nature and Mistral 7B base, this model is likely suitable for:

  • Instruction Following: Generating responses that adhere to specific user instructions.
  • Chatbots and Conversational AI: Engaging in more natural and coherent dialogues.
  • Text Generation: Creating various forms of text, from creative writing to summaries.
  • Question Answering: Providing informative answers based on given context.