longtermrisk/Qwen3-8B-risky-financial-advice-last-third-sft-seed2
TEXT GENERATIONPricing:Input $0.468 / Output $1.82Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 15, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The longtermrisk/Qwen3-8B-risky-financial-advice-last-third-sft-seed2 is an 8 billion parameter Qwen3 model developed by longtermrisk. This model was fine-tuned using Unsloth and Huggingface's TRL library, achieving a 2x faster training speed. It is specifically designed for tasks related to risky financial advice, building upon the unsloth/Qwen3-8B base model. With a context length of 32768 tokens, it is optimized for processing extensive financial text.
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Model Overview
This model, longtermrisk/Qwen3-8B-risky-financial-advice-last-third-sft-seed2, is an 8 billion parameter Qwen3-based language model developed by longtermrisk. It was fine-tuned from the unsloth/Qwen3-8B base model.
Key Characteristics
- Architecture: Based on the Qwen3 model family.
- Parameter Count: 8 billion parameters.
- Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, resulting in a 2x faster training process compared to standard methods.
- Context Length: Supports a context window of 32768 tokens.
- Specialization: The model's name suggests a specialization in generating "risky financial advice," indicating a specific domain-focused fine-tuning.
Potential Use Cases
- Financial Text Generation: Generating content related to financial advice, particularly in scenarios involving higher risk.
- Domain-Specific Research: Exploring model behavior and responses within the niche of risky financial recommendations.
- Comparative Analysis: Studying the impact of specialized fine-tuning on a base Qwen3 model for specific, potentially sensitive, domains.