localized-ft/Qwen3-32B-target-only-no-hallucination-first-third-sft-bf16
TEXT GENERATIONPricing:Input $0.408 / Cached $0.0816 / Output $1.972Concurrent Unit Cost:2Model Size:32BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 27, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The localized-ft/Qwen3-32B-target-only-no-hallucination-first-third-sft-bf16 is a 32 billion parameter Qwen3 model developed by localized-ft. This model was finetuned using Unsloth and Huggingface's TRL library, achieving 2x faster training. It is designed for specific target applications, focusing on reducing hallucinations.
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Model Overview
The localized-ft/Qwen3-32B-target-only-no-hallucination-first-third-sft-bf16 is a 32 billion parameter language model based on the Qwen3 architecture. Developed by localized-ft, this model has been finetuned from unsloth/Qwen3-32B with a specific emphasis on performance and reliability.
Key Characteristics
- Architecture: Qwen3-32B base model.
- Training Efficiency: Finetuned using Unsloth and Huggingface's TRL library, resulting in 2x faster training compared to conventional methods.
- Focus: Designed to be 'target-only' and 'no-hallucination', indicating an optimization for specific use cases where factual accuracy and reduced generative errors are critical.
Intended Use Cases
This model is particularly well-suited for applications requiring:
- High Accuracy: Scenarios where minimizing hallucinations and generating precise, factual responses is paramount.
- Efficient Deployment: Benefits from the faster training methodology, potentially leading to quicker iteration cycles for specialized applications.
- Specific Domain Tasks: Its 'target-only' nature suggests it's optimized for a defined set of tasks or domains, where its fine-tuning can be leveraged for superior performance.