kairawal/Llama-3.1-8B-Instruct-EN-SynthDolly-r16alpha32-E1-S3407

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:32kPublished:May 22, 2026License:apache-2.0Architecture:Transformer Open Weights Warm

The kairawal/Llama-3.1-8B-Instruct-EN-SynthDolly-r16alpha32-E1-S3407 is an 8 billion parameter instruction-tuned language model, developed by kairawal and fine-tuned from Meta-Llama-3.1-8B-Instruct. This model was optimized for faster training using Unsloth and Huggingface's TRL library, making it efficient for various natural language processing tasks. With a 32768 token context length, it is suitable for applications requiring robust instruction following and extended conversational capabilities.

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Model Overview

kairawal/Llama-3.1-8B-Instruct-EN-SynthDolly-r16alpha32-E1-S3407 is an 8 billion parameter instruction-tuned language model, developed by kairawal. It is fine-tuned from the Meta-Llama-3.1-8B-Instruct base model, leveraging the Llama 3.1 architecture for strong performance in conversational and instruction-following tasks. A key differentiator for this model is its optimized training process, which was accelerated using the Unsloth library in conjunction with Huggingface's TRL library, enabling faster iteration and deployment.

Key Capabilities

  • Instruction Following: Designed to accurately follow user instructions for various NLP tasks.
  • Efficient Training: Benefits from Unsloth's optimizations, allowing for quicker fine-tuning and development cycles.
  • Extended Context: Features a 32768 token context length, suitable for processing longer inputs and maintaining coherence over extended interactions.

When to Use This Model

This model is particularly well-suited for developers who:

  • Require a robust 8B instruction-tuned model for general-purpose NLP applications.
  • Value models that have undergone optimized training processes for efficiency.
  • Need a model capable of handling longer conversational turns or detailed prompts due to its substantial context window.

It offers a strong balance of performance and efficiency for a wide range of English-language generative AI use cases.