localized-ft/Qwen3-8B-target-only-no-hallucination-first-third-sft-seed4

TEXT GENERATIONPricing:Input $0.468 / Output $1.82Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 24, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

The localized-ft/Qwen3-8B-target-only-no-hallucination-first-third-sft-seed4 is an 8 billion parameter Qwen3 model, fine-tuned by localized-ft. This model was optimized for training speed using Unsloth and Huggingface's TRL library, offering a 2x faster training process. It is designed for specific target applications, focusing on reducing hallucinations and maintaining factual accuracy. With a 32K context length, it is suitable for tasks requiring extensive contextual understanding.

Loading preview...

Model Overview

The localized-ft/Qwen3-8B-target-only-no-hallucination-first-third-sft-seed4 is an 8 billion parameter Qwen3 model, fine-tuned by localized-ft. This model distinguishes itself through its optimized training process, leveraging Unsloth and Huggingface's TRL library to achieve a 2x faster fine-tuning speed compared to standard methods.

Key Characteristics

  • Base Model: Fine-tuned from unsloth/Qwen3-8B.
  • Training Optimization: Utilizes Unsloth for accelerated training, significantly reducing the time required for fine-tuning.
  • Parameter Count: 8 billion parameters, balancing performance with computational efficiency.
  • Context Length: Supports a substantial context window of 32,768 tokens, enabling processing of longer inputs and maintaining conversational coherence over extended interactions.
  • Focus: Specifically engineered to minimize hallucinations and enhance factual consistency, making it suitable for applications where accuracy is paramount.

Use Cases

This model is particularly well-suited for applications requiring:

  • Reduced Hallucinations: Ideal for tasks where generating factually accurate and non-invented information is critical.
  • Efficient Deployment: Benefits from a faster fine-tuning process, allowing for quicker iteration and deployment in specific use cases.
  • Long Context Understanding: Its 32K context length makes it effective for processing and generating content based on extensive documents or conversations.

Finetunes
2 models