phinjaz/Qwen3-4B-Petari-RL-FP8-cp200
The phinjaz/Qwen3-4B-Petari-RL-FP8-cp200 is a 4 billion parameter Qwen3-based language model, finetuned by phinjaz. This model was optimized for faster training using Unsloth and Huggingface's TRL library, building upon the unsloth/Qwen3-4B-FP8 base. It offers a 32768 token context length, making it suitable for applications requiring efficient processing of longer sequences.
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Model Overview
phinjaz/Qwen3-4B-Petari-RL-FP8-cp200 is a 4 billion parameter language model, developed by phinjaz. It is a finetuned variant of the Qwen3 architecture, specifically building upon the unsloth/Qwen3-4B-FP8 base model. This model was engineered for enhanced training efficiency, leveraging the Unsloth library and Huggingface's TRL (Transformer Reinforcement Learning) library.
Key Characteristics
- Base Architecture: Qwen3 family.
- Parameter Count: 4 billion parameters.
- Training Optimization: Utilizes Unsloth for 2x faster training and Huggingface's TRL library for finetuning.
- Context Length: Supports a substantial context window of 32768 tokens.
Potential Use Cases
This model is particularly well-suited for developers and researchers looking for:
- Efficient Finetuning: Its optimized training process makes it a good candidate for further domain-specific finetuning.
- Applications requiring long context: The 32768 token context length allows for processing and generating longer texts, such as document summarization, extended dialogue, or code analysis.
- Resource-conscious deployment: As a 4B parameter model, it offers a balance between performance and computational requirements, especially with its FP8 quantization.