wvnvwn/Mistral-7B-Instruct-v0.3-gsm8k-v1

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:7BQuant:FP8Ctx Length:4kPublished:May 16, 2026Architecture:Transformer Warm

The wvnvwn/Mistral-7B-Instruct-v0.3-gsm8k-v1 is a 7 billion parameter language model fine-tuned from mistralai/Mistral-7B-Instruct-v0.3. This model has been specifically trained using TRL for improved performance, likely in mathematical reasoning or problem-solving tasks given the 'gsm8k' indicator. It maintains a context length of 4096 tokens and is optimized for instruction-following applications.

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Model Overview

This model, wvnvwn/Mistral-7B-Instruct-v0.3-gsm8k-v1, is a specialized fine-tuned version of the Mistral-7B-Instruct-v0.3 base model by Mistral AI. It leverages the TRL (Transformers Reinforcement Learning) library for its training procedure, indicating a focus on enhancing specific capabilities through advanced fine-tuning techniques.

Key Capabilities

  • Instruction Following: Inherits and refines the instruction-following capabilities of its Mistral-7B-Instruct-v0.3 base.
  • Specialized Fine-tuning: The 'gsm8k' in its name suggests a fine-tuning objective related to mathematical word problems or general problem-solving, implying enhanced reasoning abilities in this domain.
  • Efficient Training: Developed using TRL, a framework known for efficient and effective fine-tuning of transformer models.

Training Details

The model was trained using Supervised Fine-Tuning (SFT). The development utilized specific versions of key frameworks:

  • TRL: 1.4.0
  • Transformers: 4.57.1
  • Pytorch: 2.11.0
  • Datasets: 4.8.5
  • Tokenizers: 0.22.2

Good For

This model is likely well-suited for applications requiring robust instruction adherence and potentially for tasks involving quantitative reasoning or mathematical problem-solving, given its fine-tuning context.