4fhct4sd/qwen3.5-4b-posture
The 4fhct4sd/qwen3.5-4b-posture is a 4.5 billion parameter language model, based on the Qwen/Qwen3.5-4B architecture, developed by 4fhct4sd. This model incorporates a merged LoRA adapter from a two-phase training process, specifically designed to continue scatter and crystal training. It is intended for use with Transformers 5.5.0 for inference, though no evaluation benchmarks have been run.
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Overview
This model, 4fhct4sd/qwen3.5-4b-posture, is a 4.5 billion parameter language model built upon the Qwen/Qwen3.5-4B base. It integrates a merged LoRA adapter, which is the result of a two-phase training process (scatter and crystal training).
Key Characteristics
- Architecture: Based on Qwen/Qwen3.5-4B.
- Parameter Count: 4.5 billion parameters.
- Context Length: Supports a context length of 32768 tokens.
- Training: Incorporates a merged LoRA adapter from a two-phase training process.
- Inference: Recommended for use with Transformers version 5.5.0 for optimal performance, as earlier versions (e.g., 5.3.0) showed issues with multi-turn chat.
Important Considerations
- No Benchmarks: No formal evaluation benchmarks have been run on this model, so its performance characteristics are not quantitatively verified.
- Training Data: The specific training data used for the adapter is not included in this repository.
- Limitations: The model may generate unverified claims about itself or invent conversational turns, indicating potential for hallucination. Users should be aware that generated content is not guaranteed to be factual.
When to Use
This model is suitable for developers experimenting with Qwen3.5-4B and LoRA adapters, particularly those interested in the effects of continued scatter and crystal training phases. It's important to note the lack of benchmarks and potential for conversational inaccuracies, making it more appropriate for experimental or non-critical applications where rigorous factual accuracy is not paramount.